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ISSN 0143-5124
Volume 26 Number 4/5 2005
Library Management Special issue on the Semantic Web Guest Editors: Miltiadis Lytras, Miguel-Angel Sicilia, John Davies and Vipul Kashyap
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Library Management
ISSN 0143-5124 Volume 26 Number 4/5 2005
Special issue on the Semantic Web Guest Editors Miltiadis Lytras, Miguel-Angel Sicilia, John Davies and Vipul Kashyap
Access this journal online _________________________
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Editorial advisory board __________________________
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Editorial _________________________________________
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Digital libraries in the knowledge era: knowledge management and Semantic Web technologies Miltiadis Lytras, Miguel-Angel Sicilia, John Davies and Vipul Kashyap ___
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Libraries as knowledge management centers Kevin R. Parker, Philip S. Nitse and Kay A. Flowers __________________
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Semantic Web technologies for digital libraries York Sure and Rudi Studer ______________________________________
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Applying semantic technology to a digital library: a case study Paul Warren and David Alsmeyer _________________________________
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Towards personalization in digital libraries through ontologies Nu´ria Ferran, Enric Mor and Julia` Minguillo´n_______________________
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CONTENTS
CONTENTS continued
Integrating semantic metadata in P2P-based digital libraries Hao Ding_____________________________________________________
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Building a distributed digital library for natural disasters metadata with grid services and RDF Wei Xing, Marios D. Dikaiakos, Hua Yang, Angelos Sphyris and George Eftichidis_______________________________________________
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Reasoning against a semantic digital library framework based on grid technology Ioannis Papadakis, Agapios Avramidis and Vassilis Chrissikopoulos _____
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Mining novel connections from large online digital library using biomedical ontologies Xiaohua Hu___________________________________________________
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A cooperative environment for the negotiation of term taxonomies in digital libraries Francesco Bellomi, Matteo Cristani and Roberta Cuel _________________
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On the conceptualisation of the query refinement task Nenad Stojanovic ______________________________________________
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Awards for Excellence __________________________________
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Library Management Vol. 26 No. 4/5, 2005 p. 168 # Emerald Group Publishing Limited 0143-5124
EDITORIAL ADVISORY BOARD Professor A.A. Alemna Department of Library and Archival Studies, University of Ghana Chris Batt Chief Executive, Resource: The Council for Museums, Archives and Libraries, London, UK Alex Byrne University Librarian, University of Technology, Sydney, Australia Professor Chun Wei Choo Faculty of Information Studies, University of Toronto, Toronto, Canada Professor L. Anne Clyde Faculty of Social Science, The University of Iceland, Reykjavik, Iceland Dr J. Eric Davies Director, Library and Information Statistics Unit, Loughborough University, Loughborough, UK Dr G. Edward Evans Associate Academic Vice-President for Libraries and Information Resources, Charles von der Ahe Library, Loyola Marymount University, Los Angeles, USA Dr Paul Genoni Senior Lecturer in Information Studies, Curtin University of Technology, Perth, Australia Dr G.E. Gorman Professor of Library & Information Management, School of Information Management, Victoria University of Wellington, New Zealand Sue Henczel Cataloguing, Training and Consortia Manager, Caval Collaborative Solutions, Victoria, Australia
Mag. Luis Herrera Director, Biblioteca Central, Universidad Nacional del Sur, Bahı´a Blanca, Argentina Dr Wu Jianzhong Director, Shanghai Library, Shanghai, China Professor Maurice Line Harrogate, UK Professor Peter Lor Department of Information Science, University of Pretoria, South Africa Professor Tamiko Matsumura Emeritus Professor, University of Library and Information Science, Tokyo, Japan Nick Moore Acumen, Somerset, UK Professor Dennis N. Ocholla Head, Department of Library and Information Science, University of Zululand, South Africa Professor Niels Ole Pors Royal School of Library and Information Science, Copenhagen, Denmark Bernard F. Reilly President, The Center for Research Libraries, Chicago, IL, USA Professor Amos Thapisa Director, Centre for Academic Development, University of Botswana, Gaborone, Botswana Professor Robert Usherwood Department of Information Studies, University of Sheffield, Sheffield, UK Pentti Vattulainen Director, National Repository Library, Finland
Editorial
Editorial The field of library management is broad indeed. Library managers every day confront a very difficult environment in which they are asked to deal with many issues which they may be ill-equipped to deal with. This, in many ways, is the fate of the manager. With the rapidly changing information environment it is very important to expose our readers to issues which are likely to impact on them in one way or another. This is the purpose of this issue. Library Management, in this issue, deals with the specialised area of the Semantic Web. The Semantic Web may be a topic which many are unfamiliar with but is certainly a topic with which we should acquaint ourselves. We are all dealing with the problem of digital content management but often without the theoretical and research background provided by those working on the topic. The work done by researchers in this area can and does provide most valuable insights for the professional operating in the area on a day-to-day basis. We are all dealing with metadata-intensive applications which are seeking their place in the internet sunshine. How do we achieve better hits for that information which our users require? My own work organisation seeks this resolution not only in English but also in over 70 languages! The work by our colleagues in this special issue provides many insights in to the present and future problems confronting us. The Special Interest Group on Semantic Web and Information Systems of the Association for Information Systems (SIGSEMIS) have worked with the Editor of Library Management to present this series of papers to you. The papers have not followed the usual editorial process of this journal but rather an adapted process. For this I am very grateful to Dr Miltiadis Lytras and his colleagues for the collaboration in collecting this excellent set of papers. The papers have been reviewed by the Guest Editors. The papers have also been reviewed by this Editor for quality as is usually the case. In this way they have not been through the normal processes of the Library Management Editorial Board. This is exceptional. The papers explore many subtle aspects of the Semantic Web first introduced to us by Berners-Lee in 2001. The future of this area of research remains to be seen. At least the readers of Library Management will be able to introduce or reinforce their understanding of this most important aspect of their sphere of operation. The more the sometimes diverse areas of our profession at least touch each other, the more connected will be their research outcomes.
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Steve O’Connor
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Digital libraries in the knowledge era Knowledge management and Semantic Web technologies Miltiadis Lytras ELTRUN Research Center, Athens University of Economics and Business, Athens, Greece
Miguel-Angel Sicilia Computer Science Department, University of Alcala´, Barcelona, Spain
John Davies BTExact Next Generation Web Research, Ipswich, UK, and
Vipul Kashyap Partners HealthCare System, Clinical Informatics R&D, Wellesley, Massachusetts, USA Abstract Purpose – The aim of this paper is to introduce the special issue on the Semantic Web. Intensive research has been undertaken worldwide in research centres and several achievements have been accomplished towards the ultimate objective: the expression and the exploitation of humanity’s collective knowledge. Design/methodology/approach – A multi-fold strategy for the preparation of this special issue was deployed. Given the special characteristics, a balanced mix of introductory papers to the topic, and also advanced research papers at the leading edge of Semantic Web evolution, were selected. Findings – The paper summarizes the articles of the issue and also provides help in understanding the Semantic Web Roadmap of Digital Libraries. Originality/value – Introduces the papers in the special issue. Keywords Internet, Digital libraries, Information networks Paper type Viewpoint
1. Introduction The Semantic Web vision (Berners-Lee et al., 2001) has attracted the attention of a significant and diverse collection of researchers, institutions and organizations, and a growing number of Semantic Web applications is being developed and used in diverse areas, including electronic commerce, digital libraries and e-learning, among others. All of these endeavours share the common belief that Semantic Web technology would
Library Management Vol. 26 No. 4/5, 2005 pp. 170-175 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596026
The authors would like to thank from the deepest of their hearts the Editor of Library Management, Steve O’ Connor, for his trust in their capacity and his kindness in supporting the Guest Editorial team, and also the personnel of Emerald, who did their best for the managerial issues related to the publication of this issue. A great thanks also to AIS SGISEMIS Board and members, who together cultivate the Semantic Web vision in the information systems research community.
significantly impact the use of the Web, essentially in terms of increased task delegation to intelligent software agents (Sycara et al., 2003), and a subsequent amelioration of the information overload effect (Maes, 1994). These are actually objectives sought by the artificial intelligence research community for many years, but the Semantic Web idea introduces an emphasis on the availability of shared conceptualizations described in logics-based languages, called ontologies (Gruber, 1993). Ontologies provide the common semantics that are at the hearth of automation of tasks, and metadata is provided as annotations to web resources of any kind. The well-known concept of uniform resource identifiers (URIs) defines tacitly what can be annotated, which can be any fragment of markup or content thanks to current XML-based specifications like XPath and XPointer. What can be said about web resources is only limited by the available ontologies at the moment. Since both ontologies and annotations are expressed in logics – concretely, in languages that are directly or indirectly related to description logics (Baader et al., 2003) – the typical tasks of aided search or filtering of information are built on a very different substrate than were those based on information retrieval techniques, which essentially processed human language information and some basic labels marked as metadata, e.g. HTML meta tags. In the context of libraries, the Semantic Web can be viewed as an extension of existing metadata-intensive approaches to the concept of “digital library”. Such extension should be appreciated in two dimensions. On the one hand, the Semantic Web builds on a common logics-based framework adapted to the web, which eases technology adoption while preserving a strong commitment to a formal foundation for metadata. On the other hand, the sharing of ontologies as public web resources offers new opportunities for interoperability across institutional repositories (library, cultural heritage institutions and the like) and personal or organizational records. In addition, the representation of information resources can be easily linked to metadata describing personal or group preferences, objectives or links, and even to personal relationships, as described by the Friend-of-a-Friend (FOAF) framework. These specific characteristics of the Semantic Web approach conform a paradigm for building library systems that go a step beyond the existing technological infrastructure. In this paper, an attempt to clarify the main directions for research on the concept of a “semantic digital library” is made, and the main management and technical challenges derived from such idea are determined. In the rest of this paper, the overall requirements for a semantic approach to digital libraries are sketched, and the major challenges for libraries and library management are described. 2. Objectives of a semantic approach to digital libraries Increasing amounts of digital content available anywhere and anytime makes the discussion on advanced content management technologies more relevant than ever. Metadata describing these contents, their accounting information and their relationships are the underlying infrastructure for the crafting of services that actually enhance the capabilities of individuals, as envisioned by Vannevar Bush many years ago (Bush, 1945). The overall objective for metadata-intensive applications can be summarized in two interrelated aspects: the broadness of the contents considered to fulfil a given user need, and the capability of filtering the more relevant elements
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according not only to the informative character of the information resources, but also to the cognitive preferences or context of interaction of the users. The trade-off between these two objectives is what constitutes the final quality of advanced digital library applications. In this context, Semantic Web technologies are capable of providing advanced features to a number of relevant problems, including the following: . Semantic interoperability. This can be defined as the capability of different information systems to communicate information consistent with the intended meaning of the encoded information. It entails processing of shared information so that it is consistent with the intended meaning and presentation so that it conforms to the intended meaning regardless of the source of information. Ontologies can be considered the terminological and conceptual middleware to . Delegate routine tasks to agents. Many procedures that take place in management of libraries are subject to at least partial automation. Examples are cataloguing, metadata harvesting or quality assessment based on metrics. . Provide enhanced browsing, searching and filtering user interfaces. Research in this area should investigate how well-known user seeking moves (Bates, 1990) could be supported by Semantic Web technologies, including the use of the ontologies themselves as an artefact to navigate and search resources (Garcı´a and Sicilia, 2003) In addition to the three mentioned aspects, the management of large volumes of heterogeneous assets also calls for uniform and automated digital rights managements and advanced version control features. These are areas in which the combinations of proposed common specifications like the OMA DRM with logics-based metadata could also benefit from the logics-based support provided by Semantic Web technology. Of course the benefits of the “semantic digital library” would not come without a cost. The time-consuming process of creating and managing quality metadata records for digital resources is complicated even further by the fact that metadata annotations should be expressed in reference to shared ontologies. And those ontologies are engineered artefacts that may be complex and evolving. This becomes even a more complex scenario if a broad view on semantics is considered. This is the reason why semantic tools may play a critical role in cataloguing and metadata editing interfaces, i.e. Semantic Web technologies should be used to manage their own complexity.
3. Presentation of the special issue As already mentioned in this special issue we tried to develop a reference point for Semantic Web and knowledge management for digital libraries. Given the characteristics of LM audiences we had to make a compromise: To accept papers in two general pillars: (1) High quality introductory papers that help readers to understand the notion of Semantic Web and the way it challenges digital libraries. (2) High quality advanced research papers, which provide the knowledge of applied Semantic Web and knowledge management technologies in the context of digital libraries.
We will try to provide a short description of the articles in this special issue and we will conclude with a roadmap for the Semantic Web Vision in Digital Libraries. For the first pillar we finally selected three papers, which are presented below. Kevin Parker, Philip Nitse, Kay Flowers, from Idaho State University, in their paper entitled “Libraries as knowledge management centers”, provide a thorough analysis on the requirements for a library knowledge management center (LKMC). Knowledge management, competitive intelligence services, ontologies and the Semantic Web are all considered, and the steps needed to realize a LKMC are presented In the second paper entitled “Semantic Web technologies for digital libraries”, York Sure and Rudi Studer from Institute AIFB at the University of Karlsruhe, give an excellent and informative overview of semantic web technologies. For sure this article is of critical value for the newcomers to the SW field. Paul Warren, from BT Exact, UK, and David Alsmeyer from the BT Library, UK, provide the ideal bridge for the advanced discussion of the topics in the special issue. Their paper entitled “Applying semantic technology to a digital library: a case study”, starts by describing the research challenges facing digital libraries, and goes on to discuss semantic technology and what it can offer digital libraries in general, and a case study in the context of a EU R&D funded project SEKT (http://www.sekt-project. com/). The second part of the special issue has a special emphasis on applications. Nuria Ferran, Enric Mor and Julia` Minguillo´n, from the Open University of Catalonia, in their article “Towards personalization in digital libraries through ontologies”, describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. Semantic Web and knowledge management technologies can play a significant role towards the improvement of their experience of use. In the fifth paper entitled “Integrating semantic metadata in P2P-based digital libraries”, Hao Ding, from the Information Management Group at the Norwegian University of Science and Technology, proposes a peer-to-peer (P2P) based infrastructure for managing heterogeneous schemas in digital libraries. The advantages of P2P data management are presented over the conventional methods with respect to achieving dynamic matching functionality in an open and heterogeneous digital library environment. For sure, an excellent addition to this special issue. Wei Xing, Marios D. Dikaiakos, Hua Yang, Angelos Sphyris and George Eftichidis, in the next paper entitled “Building a distributed digital library for natural disasters metadata with grid services and RDF” demonstrate how Semantic Web technologies can support a full functional digital library environment. More specifically, they present the design and implementation of a grid-based digital library for natural-disaster research metadata. When at an early stage we reviewed the title of the paper we said what a rare topic. The recent events in South-East Asia proved how important it is to invest in scientific methods and systems that provide us shields against such phenomena. Ioannis Papadakis, Agapios Avramidis and Vassilis Chrissikopoulos from the Ionian University and University of Piraeus, Greece, in the seventh paper “Reasoning
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against a semantic digital library framework based on grid technology”, propose a semantic digital library framework based on grid technology that exploits the semantics of its underlying information through the employment of adequate open standard technologies such as RDF and OWL. The next three papers are related to more advanced issues. In the eighth paper, “Mining novel connections from large online digital library using biomedical ontologies”, Xiaohua Hu, from the College of Information Science and Technology at Drexel University, USA, proposes a semantic-based knowledge discovery system for mining novel connections from large online digital libraries. Their method takes advantages of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system, Biomedical Semantic-based Knowledge Discovery System (Bio-SbKDS), is designed to uncover novel hypothesis/connections hidden in the biomedical literature. Francesco Bellomi, Matteo Cristani and Roberta Cuel, from Dipartimento di Informatica at Universita` di Verona, Italy, in their paper “A cooperative environment for the negotiation of term taxonomies in digital libraries”, propose a sophisticated system that exploits the community of practice that uses a digital library environment. Their approach is an excellent context for understanding new Semantic Web enabled services, that address the key objective of W3C under the title “Enhancing collaborative life”. Nenad Stojanovic, from the well-known Institute AIFB at the University of Karlsruhe, Germany, provides the last paper of the special issue. It is entitled “On the conceptualisation of the query refinement task”. The author proposes a novel approach for the refinement of Boolean queries by using semantic technologies, one of the most typical processes in a digital library environment. 4. Conclusions Knowledge is recognized as the ultimate resource of our days. From this perspective digital libraries provide extraordinary information and knowledge highways for all and their role towards a prosperous society based on knowledge and learning is critical. In this era of transition Semantic Web and knowledge management technologies expand the frontiers of knowledge representation and sharing. This special issue was a first step towards our objective to establish a continuous communication channel with the audiences of Library Management. We would be happy to receive your feedback on this issue and to provide any further guidance on the theme. We would like also to communicate a last piece of news. AIS SIGSEMIS, undertook the project to develop the Encyclopedia of Semantic Web Research. Semantic Web and digital libraries will be a hot topic and we encourage potential contributors to contact Dr Miltiadis D. Lytras at
[email protected] References Baader, F., Calvanese, D., McGuinness, D., Nardi, D. and Patel-Schneider, P. (Eds) (2003), The Description Logic Handbook. Theory, Implementation and Applications, Cambridge University Press, Cambridge. Bates, M.J. (1990), “Where should the person stop and the information search interface?”, Information Processing & Management, Vol. 26 No. 5, pp. 575-91.
Berners-Lee, T., Hendler, J. and Lassila, O. (2001), “The Semantic Web”, Scientific American, Vol. 284 No. 5, pp. 34-43. Bush, V. (1945), “As we may think”, Atlantic Monthly, July. Garcı´a, E. and Sicilia, M.A. (2003), “User interface tactics in ontology-based information seeking”, Psychnology E-journal, Vol. 1 No. 3, pp. 243-56. Gruber, T.R. (1993), “A translation approach to portable ontologies”, Knowledge Acquisition, Vol. 5 No. 2, pp. 199-220. Maes, P. (1994), “Agents that reduce work and information overload”, Communications of the ACM, Vol. 37 No. 7, pp. 31-41. Sycara, K., Giampapa, J.A., Langley, B.K. and Paolucci, M. (2003), “The RETSINA MAS: a case study”, in Garcia, A., Lucena, C., Zambonelli, F., Omici, A. and Castro, J. (Eds), Software Engineering for Large-Scale Multi-Agent Systems: Research Issues and Practical Applications, LNCS 2603, July, Springer-Verlag, Berlin/Heidelberg, pp. 232-50. Further reading Ayers, D. (2004), “The missing Webs”, AIS SIGSEMIS Bulletin, Vol. 1 No. 2, pp. 18-23, available at: www.sigsemis.org Bussler, C. (2004), “An interview with Christoph Bussler”, AIS SIGSEMIS Bulletin, Vol. 1 No. 2, pp. 74-80, available at: www.sigsemis.org Miller, E. (2004), “An interview with Eric Miller”, AIS SIGSEMIS Bulletin, Vol. 1 No. 2, pp. 3-10. Hendler, J. (2004), “An interview with James Hendler”, AIS SIGSEMIS Bulletin, Vol. 1 No. 1, pp. 2-4. Sheth, A. (2004), “An interview with Amit Sheth”, AIS SIGSEMIS Bulletin, Vol. 1 No. 1, pp. 14-19, available at: www.sigsemis.org Sheth, A., Ramakrishnan, C. and Thomas, C. (2005), “Semantics for the Semantic Web: the implicit, the formal and the powerful”, International Journal on Semantic Web and Information Systems, Vol. 1 No. 1, pp. 1-18.
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Department of Computer Information Systems, Idaho State University, Pocatello, Idaho, USA
Philip S. Nitse Department of Marketing, Idaho State University, Pocatello, Idaho, USA, and
Kay A. Flowers Eli M. Oboler Library, Idaho State University, Pocatello, Idaho, USA Abstract Purpose – This paper proposes enhancing libraries to act as knowledge management centers for small businesses, providing both knowledge management (KM) and competitive intelligence (CI) services. Design/methodology/approach – The requirements for a Library Knowledge Management Center (LKMC) are presented and briefly examined. KM, CI, ontologies, and the Semantic Web are all considered, and the steps needed to realize a LKMC are presented. Findings – An approach to developing a LKMC is provided, as is a rationale for the proposal. Future research issues for realization of this proposal are addressed. Research limitations/implications – This paper presents a conceptual overview of a project that is still in its early stages, and as such its practicality is difficult to evaluate. Practical implications – This proposal, if followed up with future research, will prove beneficial to both small business and to libraries. Small businesses are not always able to gather sufficient internal and external knowledge to assist in strategic planning and positioning, and thus are unable to compete with larger rivals whose resources allow them to develop sophisticated KM and CI systems. LKMCs hold promise to level the playing field. Libraries benefit because this reaffirms their relevance in a digital age in which so much information is freely available to patrons. Originality/value – This paper proposes a new service for libraries, one that will assist small businesses in competing more effectively with larger competitors. Keywords Knowledge management, Digital libraries, Internet, Small enterprises Paper type Conceptual paper
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Introduction As libraries struggle with the fallout of the digital age, they must find a creative way to remain relevant to the twenty first century user who has the ability and means of finding vast amounts of information without setting foot in a brick and mortar library. The internet and the proliferation of personal computers, both at home and at work, has lessened the need to visit the local library to look up information that had once been available only there. The freely accessible information on the web, in conjunction with the escalating costs of library materials, threatens the traditional mission of libraries to create and sustain large, self-sufficient collections for their patrons (Troll, 2002). In a seemingly unrelated arena, large companies are developing and implementing sophisticated knowledge management (KM) systems to capture, store and disseminate much- needed information gathered from their internal and external
environment. This information is used not only to streamline a company’s processes and improve their services, but it also provides intelligence about their operating environment and competitors so that the company can gain or maintain a competitive advantage. Smaller companies often lack the personnel, financial, and technical resources required to implement such a system, and as such are unable to compete at the same level as larger competitors. While all businesses require information to guide their strategic decision making, not all businesses can afford it. Therefore, an opportunity exists for local public and university libraries to provide a service to fill the gap that is developing as a result of the digital age and the need of small businesses for more information and intelligence. This paper proposes repositioning or enhancing libraries to act as KM centers for small businesses, providing both KM and competitive intelligence (CI) services. In this context, the Semantic Web offers a solution that addresses several of the problems faced by libraries in proactively cataloging and delivering information and intelligence to individuals and businesses. Today’s web lacks the capability of automatically linking documents on the basis of semantic similarities. Such linking requires human intervention to ascertain the semantic context and recognize similarities between documents. The Semantic Web is an extension to the current web designed to automatically link documents that are semantically similar (Bonner, 2002). The Semantic Web might also prove useful in connecting library functions to external systems. Using RDF and Semantic Web-based query languages, the semantics or vocabularies of various research communities may be combined to support seamless information retrieval from a single source. The Semantic Web holds promise as a tool for providing services for the growing stream of diverse web-based content available to today’s libraries (Rhyno, 2002). Libraries in the digital age In the last few years, public libraries have faced challenges from two sides. First, the expansion of the internet has created the perception that libraries are(longer needed since much content previously found only in libraries is now available on the internet. Digital resources enable users to search independently of libraries. Today’s users rely on a combination of enhanced resources, user-friendly interfaces, and improving search tools (University Library Services, 2000). This may lead to a decrease in the number of patrons who visit the library or to a decline in circulation statistics – two key measures that libraries traditionally use to demonstrate their success (Bertrot et al., 2004). In a recent survey 29 percent of respondents indicated that they perceive their usage of local libraries has decreased, with 26 percent of those respondents citing internet usage at home or work as the leading cause. However, there are some major limitations to information obtained via the internet. The most significant drawback is that it is often limited to recent information only. While the internet is good source for information from the last eight to ten years, resources prior to 1994 are most likely still in paper form. While many reference books and abstracts have made the jump to the internet and are fully available, they charge a significant fee for access, a fee usually paid by the library so that they can offer their patrons access to these resources. “Libraries of all sizes and types are embracing digital collections, although most libraries will continue to offer both print and digital collections for many years to come” (Tenopir, 2003).
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So as libraries have experienced stagnation in traditional services, they have, in many cases, increased network services and resources (Bertrot et al., 2004). Purchases of journals, magazines, and abstracting and indexing services are heavily weighted toward digital (Tenopir, 2003). Many libraries have increased their expenditures for information technology, telecommunications, and related services and resources. Network-based services and resources offered by libraries today, including digital references, digital collections, online databases, e-journals, and e-books, enable libraries to operate in an anytime/anywhere mode, allowing patrons with internet access to access content, services, and resources 365 days a year, 24 hours a day (Bertrot et al., 2004). Yet another challenge facing public libraries is the erosion of the sense of libraries as a public good. This erosion is particularly evident as libraries become candidates for major cuts or even closure as cities and towns experience flat sales tax revenue and rising costs in other areas. School, public and academic libraries in more than 40 states have absorbed more than $50 million in funding cuts in 2004, according to a national study by the American Library Association (American Library Association, 2004). Some libraries have already had choose between requesting special tax ballots or closure (Reuters, 2004). Buffalo, New York, scheduled closure of all 52 of its libraries in January 2005 (Libraries Online, 2004). Public libraries in 41 states report funding cuts of as much as 50 percent and are reducing staffs, cutting their operating hours and closing branches (American Library Association, 2004). Libraries must determine how best to react to these challenges. They must not only continue to complement their traditional role with digital collections and services, but they must also embrace opportunities to expand the services that they offer. Rigorous planning is critical to staying competitive in any information intensive and highly dynamic market. The evolution of information technology continues to redefine patterns of library usage and continues to impact users’ expectations of libraries (University Library Services, 2000). Libraries must consider their entire marketing mix for what they offer in the marketplace. They must examine their entire range of services, where they will compete, the services with which they will compete, how to best deliver their services, and who provides their competition (University Library Services, 2000). In this era of change there are opportunities for libraries to take a role in economic development to help secure their place in the community. Public libraries have a long tradition of serving businesses in their communities. Many libraries also include business collections and government documents, and some offer copy services to their patrons. The 1998 Public Library Association document, Planning for Results: A Public Library Transformation Process, lists several “components” for business and career services including assistance with job placement, resume preparation, a web site, and career guidance. As noted by the author: For the most part, these are services for the individual. They do not emphasize services of likely interest to established businesses, to community agencies involved with economic development, or to corporations (Lynch, 1998).
This demonstrates an unfilled niche that libraries can satisfy – providing services to established businesses.
Many large corporations and businesses have developed their own in-house libraries that are both digital and paper-based. As information began to “explode” in the 1950s and 1960s, these units were used to track specific information and to provide reference for researchers and others who did not have time to do the work themselves. In the late 1960s, selective dissemination of information was improved through the use of the first online database services. Searches could be entered once and saved, and then rerun against the database as information was added. These in-house libraries are highly specialized to specific industries and are forming the basis for companies to develop KM systems to capture, store and disseminate much needed intelligence for companies to gain or retain a competitive advantage in their industries. Companies are spending vast amounts of money and personnel resources on these libraries to keep abreast of the daily happenings in their very dynamic business environments. Smaller companies cannot afford the investment required to maintain an in-house library, nor do they have the personnel or resources to develop one. However, to compete with larger, more sophisticated companies in their individual industries smaller companies still need access to highly specialized KM systems. Therefore, an opportunity exists for local public and university libraries to provide a service to fill the gap that has developed as a result of the digital age and the need for more information and intelligence.
Technology and the small business Although there has been a great deal of research on the impact of technology on large companies, relatively little has focused on very small firms. It is important to acknowledge the fact that 90 percent of firms in the USA are classified as small businesses with fewer than 500 employees, and most of them have fewer than twenty employees (Zimmerer and Scarborough, 2002). This means that there is a large market for libraries to target. Several studies have shown that the size of the firm is related to adoption rates of technology (Auger and Gallaugher, 1997; Haynes et al. 1998; Department of Trade and Industry, 2000; Riquelme, 2002). The research is mixed on whether large or small firms benefit the most from technology adoption. Information technology has been recognized as an important strategic advantage to achieve a competitive advantage (Davies and Garcia-Sierra, 1999; Soliman and Janz, 2004). However, small firms often have negligible purchasing power, limited access to information, limited experience in using technology, and a lack of a long-range point of view (Davies and Garcia-Sierra, 1999; Thong, 1999; Burpitt and Rondinelli, 2000).
Knowledge management KM is the explicit and systematic management of vital knowledge (Skyrme, 1997), and includes capturing, organizing and disseminating that knowledge within an organization (Rubenfeld, 2001). KM allows organizations to generate value from their intellectual and knowledge-based assets (Santosus and Surmacz, 2001), and makes it possible to get the right information into the hands of the appropriate people at the time they need it to make decisions (Petrash, 1996).
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Importance of KM KM has several benefits and is a useful resource for any organization. KM “caters to the critical issues of organizational adaptation, survival and competence in face of increasingly discontinuous environmental change” (Malhotra, 1998). The importance of KM is corroborated by various research studies. A survey by PricewaterhouseCoopers International found that 95 percent of the CEO’s who participated saw KM as an essential ingredient for the success of their company (Suresh, 2001). Several sources (Choo, 2004; OSD Comptroller iCenter, 2002; Library Co-Op, 2004) attribute a variety of benefits to a well-planned KM effort, including the following: . KM encourages the free flow of ideas, which fosters insight and innovation and creates new value through new products or services. . KM improves customer service and efficiency by streamlining response time. . KM enhances employee retention rates by recognizing the value of employees’ knowledge and rewarding them for it. . KM streamlines operations and reduces costs by eliminating redundant or unnecessary processes and promoting reuse. . KM facilitates better, more informed decisions by reducing uncertainty. . KM contributes to the intellectual capital of an organization. . KM boosts revenues and enhances the current value of existing products by getting products and services to market faster. . KM leads to greater productivity by increasing speed of response. Resources required by KM Although KM is a useful resource, it can be prohibitively expensive for a smaller company. Qualified and dedicated personnel are required for a successful KM effort, as is supporting technology. The overall capital outlay may be beyond the means of a smaller organization. One of the resources required for a successful KM effort is personnel. KM is about what employees know, and how what they know can support business and organizational objectives. It is not a technology-driven concept, but rather it draws on human competency, intuition, ideas, and motivations (OSD Comptroller iCenter, 2002). KM requires not only knowledgeable employees, but also specialized knowledge workers. A KM worker requires varying depths of knowledge in particular areas (Skyrme, 1998): . Technical skills – information (resources) management, information technology skills. . Business knowledge – industry, markets, customers, competitors, and general business context. . Interpersonal skills – networking, listening, interpreting, challenging, teamwork, communications. . Management skills – motivating, coaching, facilitating, influencing. . Company/organization knowledge – Knowledge of procedures and culture.
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Personal characteristics – integrity, confidence, openness, trust, supportive, honesty, willingness to learn.
The financial component is also critical for the success of a KM effort. The expenditures for a serious KM effort can be quite significant. It has been estimated that the major consulting firms may spend as much as 6-12 percent of revenues on knowledge sharing programs (Gartner Group, May 28, 1998). A 2001 survey conducted by supportindustry.com and STI Knowledge reveals that 31 of 49 companies (63 percent) reporting a KM initiative spent between $100,000 and $249,000; 16 percent (eight of 49) spent $250,000 to 499,999; three of 49 spent between $500,000 and $749,999; one of 49 spent between $750,000 and $999,999; four of 49 spent between $1,000,000 and $1,999,999; and 2 of 49 spent between $2,000,000 and $4,999,999 (CRMindustry.com, 2001). None of these amounts is negligible. Another essential KM component is technology. KM tools range from standard, off-the-shelf packages to sophisticated collaboration tools designed specifically to support community building. Generally, tools fall into one or more of the following categories: knowledge repositories, expertise access tools, discussion technologies, knowledge representation, expert systems, e-learning applications, synchronous interaction tools, and data warehouse and data mining tools (Zhang, 2004; Library Co-Op, 2004). Other KM products include (Murray, 1996; Nantel, 2003): . Business intelligence (BI). . Collaboration. . Content/document management. . Data integration. . Portal. . Search/retrieval. . Decision support systems. . Database technologies. . Help-desk technology. . Brainstorming applications. . Web mapping tools. A successful KM effort can be a drain on a company’s resources. Although there are several benefits, the resources required go beyond the personnel, economic, and technical capabilities of many smaller organizations. Competitive intelligence Organizations use the CI process to gather information, to add value to it through analysis, and to report the findings to managers to solve a wide variety of problems or satisfy requests for information. CI projects range from competitive information about competitors or customers to information on mergers and acquisitions or recruiting. The types of information needed to answer these requests may include financial information, demographics, biographies, economic indicators, news articles, and customer and competitor information. Some types of information are easily gathered,
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while others require greater amounts of time and money to obtain. Once the information is secured, it must be analyzed and proper reports must be generated and disseminated to the appropriate individuals within the organization. Breeding (2000) identified several problems that users of CI have with the information that they receive from the CI process. These problems include shallowness, credibility, timeliness, focus, providers, quantity, and information sharing. The source of these problems can often be traced back to the way in which the CI process is carried out. If CI providers are consulted late in the decision-making process, shallow and poorly focused information is often the result. If sufficient time is not taken for analysis then the reports are often information-based rather than intelligence-based. In addition, the sheer quantity of information contained within the reports often overwhelms the reader. Lack of lead-time also limits the sources that can be accessed, thus calling into question the credibility of the sources of information. However, if decision-makers wait for better intelligence and analysis, it may come too late to be useful in the decision-making process. Other problems include lack of clear objectives, numerous users, massive quantities of information available, organizational barriers, lack of feedback and low budgets. Each of these problems can degrade the quality of intelligence that CI professionals can provide to users. Still, CI is important for even small organizations. Any organization that fails to monitor its environment to determine the conditions under which it must operate courts disaster (Mitroff, 1985). Identification of key economic, social, and technological issues that affect the organization, its life cycle stages, and their relevance to each other helps managers allocate attention and resources to them (McCann and Gomez-Mejia, 1992). CI is a fundamental, early step in the chain of perceptions and actions that permit an organization to adapt to its environment (Hambrick, 1981). The Semantic Web Before exploring the role of the Semantic Web in a Library Knowledge Management Center (LKMC), the Semantic Web itself must be understood. Today’s web pages are designed for human use, and human interpretation is required to understand the content. Because content is not machine-interpretable, any type of automation is difficult (Lassila, 2002). The Semantic Web augments today’s web to eliminate the need for human reasoning in determining the meaning of web-based data. The Semantic Web is based on the concept that documents can be annotated in such a way that their semantic content will be optimally accessible and comprehensible to automated software agents and other computerized tools that function without human guidance (Bonner, 2002). Thus, the Semantic Web might have a more significant impact in integrating resources that are not in a traditional catalog system than in changing bibliographic databases. For example, some scientific datasets do not have access points that convert well to bibliographic descriptions, but convey a multitude of critical concepts for the researchers that use the datasets (Rhyno, 2002). Realization of the Semantic Web relies primarily on five core technologies: XML, URIs, RDF, ontologies and intelligent agents. The extensible markup language (XML) and its accompanying technologies are the fundamental facilitator of the Semantic Web (Berners-Lee et al., 2001). XML provides for language customization through the definition of new tags to describe the data elements used in an XML document – hence the term “extensible.” Unlike HTML, which controls how data are displayed on the
web, XML is intended to facilitate the sharing of structured text and information across the internet. The data display remains the job of HTML. In short, XML and HTML perform complementary, rather than overlapping, functions. XML supplements presentation markup with markup that provides a context for understanding the meaning of the data, for example, , author . Gordon Davis , /author .. The advantage of XML is that software programs can read the specialized tags and perform operations such as extracting bibliographic information (Adams, 2002). Uniform Resource Identifiers (URIs) are another foundation of the Semantic Web (Berners-Lee and Miller, 2002). A URI is much like a URL, but it does not have to map to a real web address. Further, a URI can represent concepts (“author”), living entities (“Gordon Davis”), and virtually anything else (Rhyno, 2002). Thus, URIs provide the capability to uniquely identify not only resources, but can also indicate the relationships among resources (Berners-Lee and Miller, 2002). The Resource Description Framework (RDF) leverages URIs and XML to provide interoperability between Web applications that must exchange machine-understandable information (Brooks, 2002). RDF provides a structure that, in functional terms, expresses the meaning of web documents in a way that specialized software can understand (Adams, 2002). Through RDF, authors can specify the contents of pages and how those pages relate to one another and to other known bodies of data (Bonner, 2002). An RDF description can include various types of metadata such as the authors of the document, the date of its creation, the name of the sponsoring organization, intended audience, subject headings, etc. (Adams, 2002). The next element required for the realization of the Semantic Web is an ontology that can formally describe the semantics of classes in the many domains of interest and the semantics of properties (or attributes) used in web documents (Sadeh and Walker, 2003). Hendler (2001) defines an ontology as “a set of knowledge terms, including the vocabulary, the semantic interconnections, and some simple rules of inference and logic for some particular topic.” Ontologies allow computers to communicate with each other by providing a common set of terms – vocabularies – and rules that govern how those terms work together and what they mean. Ontologies define terms and then lay out the relationships among those terms (Adams, 2002). Web ontologies provide a shared and common understanding of specific domains that can be communicated between different application systems (Singh et al., 2005). Intelligent software agents are software entities that carry out operations and process information on behalf of a user or another program with some degree of independence or autonomy, directed by some awareness of the user’s goals or needs. Agents are used when the software must possess human-like capabilities such as the ability to perceive and assess the environment, proactive behavior in pursuing a goal, ability to learn from their experiences, and social behavior (Ermolayev et al., 2004). In the context of the Semantic Web , intelligent agents typically gather, sort and process information found on the web without human interaction. “The real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs” (Berners-Lee et al., 2001). Agents should be able to discover content that is appropriate given the customer’s preferences and requirements (Kungas and Rao, 2004).
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The Semantic Web promises to give well-defined meaning to the web by incorporating into web documents well-defined semantics. Agents should be able to determine the semantic linkages between web resources by following links from web pages to topic-specific ontologies. The meaning of vocabulary terms or XML tags used in a particular web document would be defined by a topic-specific ontology. For example, ontology cross references would make it possible for an agent to understand that “blouse” and “dress shirt” are similar concepts (Adams, 2002). Using a semantically based view of web resources, intelligent agents will be able to automatically discover, interpret, and evaluate web content (Arai et al., 2003). Components of a library knowledge management center Several steps must be undertaken for libraries to utilize these technologies in order to expand their services to act as KM centers for small businesses. First, libraries must develop domain ontologies to help categorize resources for specific businesses. A domain ontology provides a specification of a shared conceptualization to be used for formulating knowledge-level theories about a domain (Domingue and Motta, 1999; Guarino, 1997). Specific domains can be identified and a common ontology can be defined to map vocabularies of specified terms with generally accepted definitions (Gruber, 1991). “Ontologies inform the system user of the vocabulary that is available for interacting with the system and about the domain and the meaning that the system ascribes to terms in that vocabulary” (Farquhar et al., 1997). A domain ontology is an explicit formal specification of all the basic concepts (objects, concepts, and relationships) that are assumed to exist in some area of interest. The goal of domain ontologies is to specify the conceptual vocabulary and representational framework for the classes of a domain. Tools like the Ontolingua Server can assist in the construction of ontologies (Farquhar et al., 1997). Building a domain ontology requires a thorough understanding of the domain; therefore the process should start with general terms common to all small businesses, and then target specific businesses associated with individual clients, with the purpose of determining common industry terms, organization specific terms, and even project specific terms. Ontology construction can be directed through the use of needs identification tools like key intelligence topics (KITs) or the multi-class interest profile (M-CLIP), generally used in conjunction with CI. Herring (1999) proposes the concept of KITs to help identify intelligence requirements by considering strategic decisions, early-warning topics, and key players. The KITs process can help identify and define critical intelligence needs. The M-CLIP (Parker and Nitse, 2001), provides a strategically aligned framework based on the various types of information needs in order to insure that key items within each domain are accounted for. Thorough needs identification guided by a structured, multi-dimensional framework increases the likelihood of successful ontology development. A complete domain ontology encompasses a wide spectrum of corporate interests, thus providing the means to access a greater percentage of relevant information. A specialist trained in knowledge engineering can greatly assist the specification of key concepts for the domain ontology. Second, as individual documents are added to the library’s collection, natural language processing techniques can assist in determining the contents of each digital document. The library’s collection will consist of documents from both internal and
external sources. There are a variety of external sources. These may include items stored at other library locations, or items provided by pay-for-use services such as Dow Jones, Hoover’s Company Data Bank, Standards & Poor’s, NewsEdge, or free information sources such as SEC’s Edgar system, and corporateinformation.com (Breeding, 2000). The library may also subscribe to specialized databases from third-party vendors (Dialog, Lexus/Nexus), press release and newsfeed collections (WavePhore’s Newscast Access or NewsEdge’s NewsObjects), product literature, competitor Web sites, archived design specifications, company profiles and financial statements, and numerous other sources (Johnson, 1998). Internally generated knowledge refers to that knowledge within the minds of their employees. In order to handle internally generated knowledge the system should provide an interface to allow users to store information that will be sharable with other users of the system. Next, the library catalog system must be modified to store details about specific topics (concepts) and in what references to find them, because there may be many key topics or concepts in each reference. This is a significant change because libraries will(longer store only catalog details about what is in a particular reference. Finally, the Semantic Web will be used to semantically link the library’s resources, so that semantically related documents can easily be retrieved or delivered. Small businesses will pay a subscriber’s fee to use the LKMC, and will also be responsible for working with a knowledge engineer on staff to assist in specifying their information needs. Subscribers will specify multiple search parameters to narrow the search focus and target specific references. Results can be delivered on a push or pull basis to provide ongoing competitive (and other) intelligence. Alternative approaches Some libraries currently provide web portals for individuals or communities to access the internet to conduct research on topics of interest to them (Sadeh and Walker, 2003). Although people can do this from a variety of portals such as My Yahoo! or America on Line (AOL), libraries often add access to subscription databases that are too expensive for individuals or the small business owner, especially considering the amount of usage that a typical small business owner requires. The additional expense incurred by the library requires that a small business pay for the service, but the cost of access is spread over a large number of individuals or businesses. Future research Future research will address the implementation of each part of the LKMC development plan, and that research will follow a very structured approach, insuring that each individual component can be constructed before integrating the pieces into a complete system. Research areas include the development of a more specific model to guide the implementation of a KM center in the library environment. Some of the components of such a model include domain ontologies, natural language processing, a KM framework suitable for the library environment, and intelligent agents. Since the domain ontologies will be dependent on the subscribers of the system, each library will have to be able to develop ontologies that fit their subscriber base, so an appropriate framework will have to be developed. The same is true of the natural language used to develop key concepts pertinent to the subscriber base. Since a variety of KM frameworks are available, a viable framework will have to be researched, as will the
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most appropriate intelligent agents. Additional research will explore the importance of archive theory in the development of LKMCs (Nitse and Parker, 2003). Conclusion The LKMC secures the future relevance of libraries by expanding their range of services and by providing a potential source of new funding. Small businesses that cannot afford the major outflow of resources needed to maintain their own in-house KM system will be able to afford the proposed subscription or pay-as-needed service. This should increase library usage by small business and possibly even by individuals, depending on the individual nature of each LKMC. Further, the LKMC should eventually become self-sustaining or even a profit center for the library, thus allowing it to continue operating under the resource-scarce conditions that currently exist. The use of the Semantic Web in conjunction with a Knowledge Management system that provides Competitive Intelligence, with output directed at small and medium size businesses should provide the direction needed for modern libraries to survive in the digital age. The LKMC expands the services offered by libraries and allows them to provide a vital resource to the business community. Further, it provides a service that enables small businesses to compete on a more even footing with larger rivals. The LKMC serves to insure the continued viability of libraries in this ever-changing environment. References Adams, K.C. (2002), “The Semantic Web adds logic to web services”, KM World, Vol. 11 No. 3. American Library Association (2004), ALA’s Report on Library Funding in the United States, American Library Association, Chicago, IL. Arai, S., Murakami, Y., Sugimoto, Y. and Ishida, T. (2003), “Semantic web service architecture using multi-agent scenario description”, Pacific Rim International Workshop on Multi-Agents (PRIMA 2003), Lecture Notes in Artificial Intelligence, Vol 2891, Springer-Verlag, Berlin, pp. 98-109. Auger, P. and Gallaugher, J.M. (1997), “Factors affecting the adoption of an internet-based sales presence for small businesses?”, The Information Society, Vol. 13 No. 1, pp. 55-74. Berners-Lee, T. and Miller, E. (2002), “The Semantic Web lifts off”, ERCIM News, 51, October, pp. 9-11. Berners-Lee, T., Hendler, J. and Lassila, O. (2001), “The Semantic Web”, Scientific American, Vol. 284 No. 5, pp. 34-43. Bertrot, J.C., McClure, C.R., Davis, D.M. and Ryan, J. (2004), “Capture usage with e-metrics”, Library Journal, available at: www.libraryjournal.com/article/CA411564?display ¼ FeaturesNews&industry Bonner, P. (2002), “The Semantic Web”, PC Magazine, Vol. 21 No. 13, pp. IP01-IP04. Breeding, B. (2000), “CI and KM convergence: a case study at Shell Services International”, Competitive Intelligence Review, Vol. 11 No. 4, pp. 12-24. Brooks, T.A. (2002), “The Semantic Web, universalist ambition and some lessons from librarianship”, Information Research, Vol. 7 No. 4. Burpitt, W.J. and Rondinelli, D.A. (2000), “Small firms’ motivations for exporting: to earn and learn?”, Journal of Small Business Management, Vol. 38 No. 4, pp. 1-14.
Choo, C.W. (2004), “FAQs on knowledge management”, available at: http://choo.fis.utoronto.ca/ KMfaq/ CRMindustry.com (2001), “Results of research – e.Support & knowledge management”, available at: www.crmindustry.com/industry_research/kmgraphs.htm, p. wwwcrmindustrycom/ industry_research/kmgraphshtm Davies, A.J. and Garcia-Sierra, A.J. (1999), “Implementing electronic commerce in SMEs – three case studies”, BT Technology Journal, Vol. 17 No. 3, pp. 97-111. Domingue, J. and Motta, E. (1999), “A knowledge-based news server supporting ontology-driven story enrichment and knowledge retrieval”, Proceedings of the 11th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW 1999), Dagstuhl Castle, Germany, Springer-Verlag, Berlin, pp. 103-20. Department of Trade and Industry (2000), International Bench Marking Survey for ICT Use, Department of Trade and Industry, London, available at: www.ukonline.gov.uk Ermolayev, V., Keberle, N., Plaksin, S., Kononenko, O. and Terziyan, V.Y. (2004), “Towards a framework for agent-enabled semantic web service composition”, International Journal of Web Services Research, Vol. 1 No. 3, pp. 63-87. Farquhar, A., Fikes, R. and Rice, J. (1997), “The ontolingua server: a tool for collaborative ontology construction”, International Journal of Human-Computer Studies, Vol. 46 No. 6, pp. 707-28. Gruber, T. (1991), “The role of common ontology in achieving sharable, reusable knowledge bases”, in Allen, J.A., Fikes, R. and Sandewall, E. (Eds), Principles of Knowledge Representation and Reasoning, Morgan Kaufmann, San Mateo, CA, pp. 601-2. Guarino, N. (1997), “Semantic matching: formal ontological distinctions for information organization, extraction, and integration”, in Pazienza, M.T. (Ed.), Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, Springer Verlag, Berlin, pp. 139-70. Hambrick, D.C. (1981), “Specialization of environmental scanning activities among upper level executives”, Journal of Management Studies, Vol. 18 No. 3, pp. 299-320. Haynes, P.J., Becherer, R.C. and Helms, M.M. (1998), “Small and mid-sized businesses and internet use: unrealized potential?”, Internet Research, Vol. 8 No. 3, pp. 229-35. Hendler, J. (2001), “Agents and the semantic web”, IEEE Intelligent Systems, Vol. 16 No. 2, pp. 30-7. Herring, J.P. (1999), “Key intelligence topics: a process to identify and define intelligence needs”, Competitive Intelligence Review, Vol. 10 No. 2, pp. 4-14. Johnson, A.R. (1998), “An introduction to knowledge management as a framework for competitive intelligence”, paper presented at the International Knowledge Management Executive Summit, San Diego, CA. Kungas, P. and Rao, J. (2004), Proceedings of the Fifth International Conference on Web-Age Information Management, Dalian, China, July 15-17, LNCS 3129, Springer-Verlag, Berlin. Lassila, O. (2002), “Towards the Semantic Web”, paper presented at Towards the Semantic Web and Web Services Conference, Helsinki, October 21-22. Library Co-Op (2004), “Knowledge management FAQ”, available at: www.thelibraryco-op.com/ KMFAQ.htm Libraries Online (2004), “County budget cuts will force Buffalo libraries to close”, Libraries Online, November 5, available at: www.ala.org/al_onlineTemplate.cfm?Section ¼ november 2004abc &Template ¼ /ContentManagement/ContentDisplay.cfm&ContentID ¼ 79495
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Mitroff, I.I. (1985), “Two fables for those who believe in rationality”, Technological Forecasting and Social Change, Vol. 28, pp. 195-202. Murray, P.C. (1996), “What to know before you select knowledge management technology”, Knowledge Management Briefs, Vol. 1 No. 4, available at: http://www.ktic.com/TOPIC7/ 14_TECH.HTM Nantel, R. (2003), Knowledge Management Tools and Technology 2004: 35 Systems to Maximize Your Organization’s Intellectual and Human Capital, brandonhall.com, Sunnyvale, CA, available at: www.brandonhall.com/public/publications/KM2004/index.htm Nitse, P.S. and Parker, K.R. (2003), “Library science, knowledge management, competitive intelligence: archive theory the common link”, The Reference Librarian, Vol. 38 Nos. 79/80, pp. 395-407. OSD Comptroller iCenter (2002), Knowledge Management: Maximizing Human Potential, The LearningSource at the OSD Comptroller iCenter, available at: www.dod.mil/comptroller/ icenter/learn/knowledgemanconcept.pdf Parker, K.R. and Nitse, P.S. (2001), “Improving competitive intelligence gathering for knowledge management systems”, Proceedings of the 2001 International Symposium on Information Systems and Engineering – ISE’2001-Workshop: Knowledge Management Systems: Concepts, Technologies and Applications, Las Vegas, Nevada. Petrash, G. (1996), “Managing knowledge assets for value”, paper presented at the Knowledge-Based Leadership Conference, Linkage Inc. Boston, MA. Rhyno, A. (2002), “The Semantic Web and libraries”, InsideOLITA, available at: www.hpl. hamilton.on.ca/OLITA/InsideOLITA/IO2002No6.htm Riquelme, H. (2002), “Commercial internet adoption in China: comparing the experience of small, medium and large businesses”, Internet Research, Vol. 12 No. 3, pp. 276-86. Rubenfeld, J. (2001), “Knowledge management for life: make the world a better place”, Ubiquity, Vol. 2 No. 41. Reuters (2004), “Steinbeck’s Calif. hometown may close libraries”, Reuters, November 20. Sadeh, T. and Walker, J. (2003), “Library portals: toward the Semantic Web”, New Library World, Vol. 104 Nos. 1184/1185, pp. 11-19. Santosus, M. and Surmacz, J. (2001), “The ABCs of knowledge management”, available http:// www.cio.com/research/knowledge/edit/kmabcs.html Singh, R., Iyer, L. and Salam, A.F. (2005), “Semantic eBusiness”, International Journal on Semantic Web & Information Systems, Vol. 1 No. 1, pp. 19-35. Skyrme, D. (1997), “Knowledge management: making sense of an oxymoron”, Management Insights, No. 2, available http://www.skyrme.com/insights/ 22km.htm Skyrme, D. (1998), “Knowledge management – a fad or a ticket to ride?”, available at: www. skyrme.com/pubs/iis0298.htm Soliman, K.S. and Janz, B.D. (2004), “An exploratory study to identify the critical factors affecting the decision to establish internet-based interorganizational information systems”, Information & Management, Vol. 41 No. 6, pp. 697-706.
Suresh, R. (2001), “Knowledge management: an overview”, available at: www.providersedge. com/docs/km_articles/km_an_overview.pdf Tenopir, C. (2003), Use and Users of Electronic Library Resources: An Overview and Analysis of Recent Research Studies, Council on Library and Information Resources, Washington, DC, available at: www.clir.org/pubs/reports/pub120/pub120.pdf Thong, J.Y.L. (1999), “An integrated model of information systems adoption in small businesses”, Journal of Management Information Systems, Vol. 15 No. 4, pp. 187-214. Troll, D.A. (2002), “How and why libraries are changing: what we know and what we need to know”, Libraries and the Academy, Vol. 2 No. 1, pp. 97-121. University Library Services (2000), ULS Strategic Plan 1999-2000: Integrating the Library and the University, University Library Services, available at: www.library.vcu.edu/admin/ stratplan /draft1/SP3.html Zhang, B. (2004), “Knowledge management overview”, available at: http://ebusiness. insightin.com/knowledge_management/knowledge_management_overview.html Zimmerer, T.W. and Scarborough, N.M. (2002), Essentials of Entrepreneurship and Small Business Management, 3rd ed., Pearson Education, Upper Saddle River, NJ.
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Semantic Web technologies for digital libraries York Sure and Rudi Studer
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Institute AIFB, University of Karlsruhe, Karlsruhe, Germany Abstract Purpose – The purpose of this article is to provide an overview about the Semantic Web, its importance and history and an overview of recent Semantic Web technologies which can be used to enhance digital libraries. Design/methodology/approach – The paper answers, at least partially, questions like “What is the Semantic Web?”, “How could the Semantic Web look like?”, “Why is the Semantic Web important?”, “What are ontologies?” and “Where are we now?”. Several pointers to further literature and web sites complete the overview. Findings – Semantic Web technologies are valuable add-ons for digital libraries. There already exist numerous academic and commercial tools which can be applied right now. Practical limitations/implications – The overview of Semantic Web technologies cannot be complete in such an article, therefore we limit ourselves to the most prominent technologies available. However, following the pointers given readers can easily find more information. Originality/value – The article is of particular value for newcomers in this area. Keywords Internet, Digital libraries, Generation and dissemination of information Paper type General review
What is the Semantic Web? Berners-Lee et al. (2001) describe the Semantic Web as: “. . .an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation”. The key enabler of the Semantic Web is the need of many communities to put machine-understandable data on the web which can be shared and processed by automated tools as well as by people. Machines should not just be able to display data, but rather be able to use it for automation, integration and reuse across various applications. The European Commission is funding numerous projects related to ontologies and the Semantic Web in its currently running Sixth Framework Research Programme, e.g. “Semantically Enabled Knowledge Technologies” (the SEKT project[1]). The worldwide Semantic Web community is growing rather fast and forces are being joined with other technology developments such as web services or multimedia. Last, but not least, vendors are already offering mature products and solutions based on semantic technologies. Thus, the Semantic Web is currently moving from being a vision to becoming reality.
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How could the Semantic Web look like? Even worse: “How would you explain the Semantic Web to your grandparents?” Answering this question is one of the challenges for participants of the Semantic Web Challenge[2]. It might be questionable whether grandparents would understand the aim of, for example, the winning application in 2003, namely that it “combines information from multiple heterogeneous sources, such as published RDF sources,
personal web pages, and data bases in order to provide an integrated view of this multidimensional space”[3]. Nevertheless, it offers the flavour of current Semantic Web technologies. A very illustrative and at the same time amusing article gives a glimpse into the far future, namely: “August 2009: how Google beat Amazon and Ebay to the Semantic Web” (Ford, 2002).
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191 Why is the Semantic Web important? To illustrate the potential importance of the Semantic Web we will start with some quotes showing the relevance and awareness the Semantic Web already has at non-academic key players. The way software and devices communicate today is doomed. To interoperate on the X Internet, they’ll use decentralized data dictionaries based on emerging Semantic Web technologies (Truog, 2001). While the industry is busy creating the underpinnings of open computing with standards like eXtensible Markup Language, still missing are what Plattner calls “semantic” standards, or how to make different computers recognize data about a business partner, a customer, or an order and know what to do with it. In other words, said Plattner, the software industry is building an alphabet but hasn’t yet invented a common language (Hasso Plattner, SAP, in CNet News, 2002).
Little history of the Semantic Web The advent of the world wide web (WWW) gave mankind an enormous pool of available information. The WWW is based on a set of established standards, which guarantee interoperability at various levels:, e.g. the TCP/IP protocol provides a basis for transportation of bits, on top HTTP and HTML provide a standard way of retrieving and presenting hyperlinked text documents. Applications could easily make use of this basic infrastructure, which led to the now existing WWW. However, nowadays the sheer mass of available documents and the insufficient representation of knowledge contained in documents make “finding the right things” real work for human beings. A major shortcoming of HTML is that it is well suited for human consumption, but not for machine-processability. As such, to interpret the information given in documents the human has always to be in the loop. To overcome such shortcomings, ontologies recently have become a topic of interest in computer science. Ontologies provide a shared understanding of a domain of interest to support communication among human and computer agents, typically being represented in a machine-processable representation language. Thus, ontologies are seen as key enablers for the Semantic Web. What are ontologies? There are different definitions in the literature of what an ontology should be, the most prominent being published by Gruber (1995): An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. For AI systems, what “exists” is that which can be represented.
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A conceptualization refers to an abstract model of some phenomenon in the world by identifying the relevant concept of that phenomenon. Explicit means that the types of concepts used and the constraints on their use are explicitly defined. This definition is often extended by three additional conditions: “An ontology is an explicit, formal specification of a shared conceptualization of a domain of interest”. Formal refers to the fact that the ontology should be machine readable (which excludes for instance natural language). Shared reflects the notion that an ontology captures consensual knowledge, that is, it is not private to some individual, but accepted as a group. The reference to a domain of interest indicates that for domain ontologies one is not interested in modelling the whole world, but rather in modelling just the parts which are relevant to the task at hand. In a nutshell, ontologies help to represent knowledge in a machine processable way; they express a shared view on a domain of interest. How can Semantic Web technologies help digital libraries? Digital libraries offer access to large amounts of content in form of digital documents. Many of them have evolved from traditional libraries and concentrated on making their information sources available to a wider audience, e.g. by scanning journals and books, thereby only taking limited advantage of the benefits modern computing technologies offer. To overcome this bottleneck, research and development for digital libraries include processing, dissemination, storage, search and analysis of all types of digital information. Semantic technologies allow for the description of objects and repositories, i.e. the need to establish common schemes in form of ontologies, e.g. for the naming of digital objects. A main goal is to enable interoperability, i.e. the ability to access, consistently and coherently, similar classes of digital objects and services, distributed across heterogeneous repositories. Typical usage scenarios for Semantic technologies in digital libraries include among others user interfaces and human-computer interaction (displaying information, allowing for visualization and navigation of large information collections), user profiling (taking into account the overall information space), personalization (balancing between individual and community-based personalization), and user interaction. These and other challenges are addressed by the SEKT project guided by the vision that in future, while there would still be many digital repositories, a digital library system should provide a consistent view of as many repositories as possible. From a user’s perspective, they should appear to be a single digital library system. Even more, a digital library system needs to extend smoothly from personal information sources, workgroup and corporate systems, out to personal views of the content of more public digital libraries. Further information about this SEKT case study can be found in the following article in this issue, “Applying semantic technology to a digital library: a case study”. Which are prominent Semantic Web technologies? Ontobroker (initially developed at the Institute AIFB/University of Karlsruhe, and now commercialized by the company Ontoprise) and SHOE (University of Maryland) were two ontology-based systems ahead of their time. Both systems relied on additional
semantic markup which was put into regular web pages, so-called annotations. The systems showed very early the feasibility of adding machine-processable semantics to web pages. Many ideas of this work made it into current Semantic Web standards of the W3C (see the later section on Standards). Both systems also heavily influenced the development trends of semantic technologies. In the following we will briefly characterize typical Semantic Web tools and give examples of existing commercial and academic tools. It is quite noteworthy that most tools are currently not only being used to build and maintain WWW applications, but also corporate intranet solutions. Ontology editors Ontology editors allow for creation and maintenance of ontologies, typically in a graphically oriented manner. There exists a plethora of available implementations, each having its own specialty and different functionalities. Common to most editors is the ability to create a hierarchy of concepts (such as “Car is a subconcept of Motor Vehicle”) and to model relationships between those concepts (such as “A car is driven by a person”. More advanced editors also allow the modelling of rules, but to explain this is beyond the scope of this paper. OntoEdit is the most prominent commercial ontology editor (available at: www. ontoprise.com). Unlike most other editors, OntoEdit comes with a strong inferencing backbone, Ontobroker, which allows the modelling and use of powerful rules for applications. Numerous extensions, so-called plug-ins, exist to adapt OntoEdit flexibly to different usage scenarios such as database mapping. Last, but not least, a fullfledged tool support is provided by the Ontoprise team which makes it attractive for companies. Prote´ge´ is the most well-known academic ontology editor with a long development history (available at: http://protege.stanford.edu/). Similar to OntoEdit it is based on a flexible plug-in framework. Numerous plug-ins have been provided so far which nicely demonstrate possible extensions for typical ontology editors. An example is the PROMPT plug-in, which allows for merging of two given ontologies into a single one. KAON (http://kaon.semanticweb.org) is not only an ontology editor, but rather an open-source ontology management infrastructure targeted at business applications. It includes a comprehensive tool suite allowing easy ontology creation and management, as well as building ontology-based applications. An important focus of KAON is on integrating traditional technologies for ontology management and application with those used in business applications, such as relational databases. Annotation tools Annotation tools (see also Handschuh and Staab, 2003) allow for adding semantic markup to documents or, more generally, to resources. The great challenge here to automate the annotation task as much as possible to reduce the burden of manual annotation for large-scale resources. A good place to find further information on annotation and authoring, a quite related topic, is http://annotation.semanticweb.org/. Annotea (http://www.w3.org/2001/Annotea/) is a LEAD (Live Early Adoption and Demonstration) project enhancing the W3C collaboration environment with shared annotations. By annotations we mean comments, notes, explanations, or other types of external remarks that can be attached to any web document or a selected part of the
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document without actually needing to touch the document. When the user gets the document he or she can also load the annotations attached to it from a selected annotation server or several servers and see what his peer group thinks. OntoMat-Annotizer (http://annotation.semanticweb.org/ontomat) is currently the most prominent annotation tool. It is based on a full-fledged annotation framework called CREAM, which is already being extended to support semi-automatic annotations of documents as well as annotation of databases. KIM (http://www.ontotext.com/kim) provides a knowledge and information management (KIM) infrastructure and services for automatic semantic annotation, indexing, and retrieval of unstructured and semi-structured content. Inference engines Inference engines allow for the processing of knowledge available in the Semantic Web. In a nutshell, inference engines deduce new knowledge from already specified knowledge. Two different approaches are applicable here: having general logic based inference engines, and specialized algorithms (problem-solving methods). Using the first approach one can distinguish between different kinds of representation languages such as higher order logic, full first order logic, description logic, datalog and logic programming (see also http://semanticweb.org/inference.html). Recently, in a contest-like project three state-of-the-art inference engines were evaluated with quite interesting results (see also http://www.projecthalo.com/). Inference engines are per se very flexible and adaptable to different usage scenarios such as information integration or, to show the bandwidth of possible scenarios, intelligent advisors. Ontobroker (http://www.ontoprise.com) is the most prominent and capable commercial inference engine. It is based on frame logic, tightly integrated with the ontology engineering environment OntoEdit and provides connectors to typical databases. It was already used in numerous industrial and academic projects. FaCT (http://www.cs.man.ac.uk/ , horrocks/FaCT/)is one of the most prominent Description Logics based inference engines. In a nutshell, FaCT (fast classification of terminologies) is a description logic classifier that can also be used for modal logic satisfiability testing. It is based on the tableaux calculus. KAON2 (http://kaon2.semanticweb.org/) is a new description logics based inference engine for OWL-DL and OWL-Lite reasoning. Reasoning is implemented with novel algorithms which reduce a SHIQ(D) knowledge base to a disjunctive datalog program. Where are we now? Standards activities for Semantic Web languages are mainly driven by working groups of the W3C (http://www.w3c.org/). The Semantic Web layer cake (see Figure 1) by Tim Berners-Lee shows the layering of the current state-of-the-art and future planned standards. On the right side can be seen the current status of each layer. While XML as a baseline allows for a syntactical description of documents, the layers RDF, Ontology and Logic are adding machine-processable semantics – a necessary prerequisite for, for example, shareable web resources. On top of the core standards for XML (eXtensible Markup Language) and RDF (Resource Description Framework) the W3C WebOnt working group (http://www.w3. org/2001/sw/WebOnt) released early 2004 the OWL web ontology language standard
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Figure 1. The Semantic Web layer cake
(http://www.w3.org/TR/owl-ref). Future work remains to be done for the logic, proof and trust layers. The community is growing quickly, also attracting researchers and practitioners from other areas such as information systems (e.g. the AIS SIG on Semantic Web and information systems (see http://www.sigsemis.org/). The upcoming conferences ESWC2005 (2nd European Semantic Web Conference (see http://www.eswc2005.org)) and ISWC2005 (4th International Semantic Web Conference (see http://iswc2005. semanticweb.org)) will be good places to find latest research results and industrial applications of Semantic Web technologies. Notes 1. EU IST SEKT project, see www.sekt-project.com 2. Semantic Web Challenge, initiated in cooperation with the International Semantic Web Conference in 2003, continued in 2004, see also http://challenge.semanticweb.org/ 3. CS AKTiveSpace Tour, see also http://triplestore.aktors.org/SemanticWebChallenge/ References Berners-Lee, T., Hendler, J. and Lassila, O. (2001), “The Semantic Web”, Scientific American, May, available at www.sciam.com/2001/0501issue/0501berners-lee.html CNet News (2002), “CNet News SAP calls for software that works together”, CNet News.com, 27 March. Ford, P. (2002), “August 2009: how Google beat Amazon and Ebay to the Semantic Web”, Ftrain.com, 26 July, available at: www.ftrain.com/google_takes_all.html Gruber, T.R. (1995), “Towards principles for the design of ontologies used for knowledge sharing”, International Journal of Human-Computer Studies, Vol. 43 Nos. 5/6, pp. 907-28. Handschuh, S. and Staab, S. (Eds) (2003), “Annotation for the Semantic Web”, Frontiers in Artificial Intelligence and Applications, Vol. Vol. 96,. Truog, D. (2001), “How the X Internet will communicate”, Forrester Report, December.
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Applying semantic technology to a digital library: a case study Paul Warren BT Next Generation Web Research Team, Ipswich, UK, and
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David Alsmeyer BT Library, Ipswich, UK Abstract Purpose – To describe how semantic knowledge technology can be used to enhance a digital library. Design – The paper examines the main research challenges in the field of digital libraries and identifies the extent to which semantic knowledge technology can be used to respond to these challenges. Functionality developed within the SEKT project (http://sekt.semanticweb.org) is used to enhance an existing digital library. The development of a good ontology is central to such an application, and the paper describes the particular ontology engineering approach adopted. Findings – Four broad challenges were identified: achieving interoperability; describing objects and repositories; managing multimedia collections; and improving user interfaces and human-computer interaction. The SEKT digital library case study is using semantic knowledge technology to respond to the first two and the last of these. The paper describes how this is being done and the kind of enhanced functionality being developed. Originality/value – The paper is of value in understanding how semantic knowledge technology can enhance information management in general and a digital library in particular. Keywords Digital libraries, Internet, Knowledge management Paper type General review
Introduction This paper describes how semantic knowledge technology has the capability to enhance a digital library, and how this capability is being realised in one particular digital library. This is being done as part of the SEKT project[1], which is developing such technology for a range of applications. The paper starts by describing the research challenges facing digital libraries; and goes on to discuss semantic technology and what it can offer digital libraries in general in general, and our case study in particular. Digital libraries – a research agenda Over the last decade and a half there has been considerable research activity in the field of digital libraries. Much of this has been focussed on specific goals, e.g. the preservation of cultural heritage and the implementation of multilinguality. However, in a workshop held in 1995, the US National Science Foundation sought to identify the key research challenges for digital libraries (Lynch and Garcia-Molina, 1995): Library Management Vol. 26 No. 4/5, 2005 pp. 196-205 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596053
The SEKT project is funded by the European Commission under the 6th Framework Programme (IST-2003-506826). The authors would like to thank their colleagues who have contributed to this work, in particular Ian Thurlow and Nick Kings for developing the domain ontology, and Allyson Cheung for her work on the state of the art survey.
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Interoperability. The ability of digital libraries to exchange and share documents, queries and services. The term also encompasses the ability to generate a single view on different library components without forfeiting independence. Description of objects and repositories. The need to establish common schemes for the naming of digital objects so as to facilitate search and retrieval from disparate distributed sources. Collection management and organisation. The ability to store, index and retrieve non-textual and multimedia content. User interfaces and human-computer interaction How information is displayed and visualised, and how the user navigates large information collections.
Semantic knowledge technology has something to offer in response to all of these challenges. Indeed, in the same workshop, the “grand challenge” of digital libraries was identified as deep semantic interoperability. This was seen as the ability to access, consistently and coherently, similar classes of digital objects and services, distributed across heterogeneous repositories with mediating software to compensate for site by site variations. More recently, a joint working group between the US National Science Foundation and DELOS[2], a “network of excellence” in the area of digital libraries identified a set of research challenges specific to the field of “personalisation and recommender” systems (Callan and Smeaton, 2003): . Modelling users. The view was expressed that currently very simple approaches need to become more realistic. . Personalisation. There was a stated need to research the difference between ephemeral and persistent needs, supporting the balance between individual and community-based personalisation. . User interaction. In particular, the need to promote a more flexible approach to recommendation and feedback and the integration of human and automated reasoning. . Evaluation. The view was that personalisation should be assessed from the perspective of the individual, the individual as a member of a community, and the community as a whole. . Social effects. Here the need is to achieve a balance between privacy and collaboration by developing privacy protection while supporting collaboration and information sharing. In this paper we shall see how the SEKT project is developing technology to address these challenges. Implementing semantic technology At its simplest level, applying semantic technology means using metadata to describe the meaning of objects. This is typically contrasted with the use of HTML today, where the metadata describes how information objects are to be formatted but says nothing about their meaning. Here the objects may be documents; sections within documents, or information entities such as the name of a person or company, or a role (e.g. “CEO”).
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The objects may also be multimedia, e.g. still or moving images, although the work described here is essentially concerned with text. The basic building block of semantic technology is the use of eXtensible Markup Language (XML) which permits the use of tags to describe objects. A typical XML element, to describe an attribute of this paper, might be , author . Paul Warren , /author . . Of course, whilst this is intuitively obvious, in practice much effort has been put into developing agreed terms such as “author” in our example. XML is now widely used, and in particular is the basis of a large number of standardised languages over a range of business activities. However, XML lacks any facilities for modelling relationships between the entities described. Thus we have no way of saying anything about “Paul Warren”, e.g. that he is employed by BT, that he has written other articles etc. To overcome this limitation, Resource Description Framework (RDF) was designed. This is an extremely simple modelling language conceivable, based on the use of “subject, verb, object” triples (e.g. “Paul Warren”, is employed by, “BT”). In principle RDF is independent of XML, and in fact a set of interrelated RDF triples is most naturally represented as a graph. However, RDF is commonly “serialized” using XML. This takes us further in our quest to describe semantics. However, whilst RDF permits data modelling, it does not enable us to define any semantics. We want, for example, a notion of class, so that “Paul Warren” can be regarded as an instance of the class “Person” and “BT” is an instance of the class “Company”. We want to define relationships, e.g. is employed by as having a specific domain (the instances in the class “Person”) and range (the instances in the class “Company”). This we can do with RDF Schema[3] (RDFS). In fact, we use RDFS to describe an ontology. Ontologies have been studied as a topic in philosophy since Ancient Greece. More recently they have been used by computer scientists. In this context probably the most frequently quoted definition of an ontology is “a specification of a conceptualisation” (Gruber, 1993). More concretely an ontology can be viewed as a generalisation of a taxonomy, as shown in Figure 1. At the top of Figure 1 is a simple taxonomy of companies, divided into public and private, and with the former subdivided into those listed on the New York Stock Exchange and the London Stock Exchange – for simplicity all other stock exchanges worldwide are omitted. Below we have a much richer structure. The simple taxonomy of companies has been expanded to include the class of EU companies and that of US companies, so that “companies on the LSE” and “companies on NYSE” are each a subclass of two superclasses. The existence of more than one superclass of a given class is a feature not normally found in conventional taxonomies. We also now have two relationships. One, sells to, is between companies. The other “operates in” has as its subject a company and its object an industry. Note that properties, including the ability to participate in relationships, are inherited by subclasses. Note also that, since “companies on LSE” has two superclasses, it can inherit properties from both. Developments in ontology languages enable the relationships to be described, e.g. as being transitive or symmetric. Moreover, recent theoretical work has provided a clear understanding of how the use of given language constructs affect computational tractability.
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Figure 1.
In fact, RDFS is a very limited language for describing ontologies and a more descriptive language, OWL, has now been defined by the World Wide Web Consortium. That said, for knowledge management applications much can be achieved with RDFS. With rich descriptive power comes additional computational complexity. To reflect this there are a variety of species of OWL. The simplest of these, OWL Lite, is a natural starting point for those wishing to go beyond the limitations of RDFS. For a good introduction to the whole subject of these semantic web languages from XML through to OWL (see Antoniou and van Harmelen, 2004). Tools are already being developed for working with OWL, see for example (Kalyanpur et al., 2005). Enhanced functionality Using the languages and the approach described above, we can tackle some of the key challenges of digital libraries. With ontologies we now have a rich framework for describing information objects and for searching and browsing them. We can create richer descriptions of documents than is possible simply with keywords. This in itself enhances search and browsing. We can, for example, have a hierarchy of topics to describe our documents. If a search for documents on a particular topic produces too many hits, we can move down the topic hierarchy to be more specific. If it produces too few hits, we can relax the search criterion by moving up the topic hierarchy. Moving up or down such a topic hierarchy can be under the user’s control, or it can be done automatically in response to the system’s view of what constitutes too many or too few hits. We can create ontologies not just to search for documents, but also to search for information entities within documents. We can create an ontology to describe people, companies, industries, their various relationships etc. We imagine searching for a company name. Frequently a company name is also used to describe other entities besides companies, and perhaps is used for several companies in different industries. If
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the information entities within our document corpora are described using an appropriate ontology, we can specify that we are searching for a company, and even that we are seeking a company in a specific industry. Moreover, we can reason with ontologies. As a simple example, equivalences can be defined between terms in the same or different ontologies. These equivalences can then be used as input to a reasoner to make simple inferences. In fact it is anticipated that users will want both conventional textual search as well as ontological search. Different users will have different preferences, and even the same user may vary his strategy during a search. Overall, though, the goal is to create semantic searching whereby the information found does not depend upon the accident of terminology used, but upon the underlying meaning sought. We do not see retrieving documents as an end in itself. This is not what the user wants. The user wants knowledge. We envisage using the ontological descriptions of our knowledge to meaningfully combine and summarise information from a variety of documents. So that when the user searches for information about a particular company, he receives a summary based on information from a variety of sources. The ontology used to describe documents and information entities can also be extended to describe the users. In particular, the same topic hierarchy used to describe documents can be used to describe user interests. The user himself or herself can create such a profile, but it can also be done automatically based on the documents which the user has downloaded. Practical implementations are likely to use a combination of manual and automatic approaches. The user may provide an initial set of interests and the system will suggest additions, or even deletions, from time to time. In principle the additions at least could be done automatically. However, feedback from users suggests that many wish to stay “in control” and want the power to accept or reject any change to their profile. Once a user profile has been created it can have a variety of uses. The most obvious is to generate alerts as new material is available in the library. Such systems do already exist, of course. However, current systems are based on keywords. With semantic alerting a user may be notified of a document despite the document not containing any search terms employed by the user, or any terms commonly found in the documents read by the user. The system can deduce that a document is about a topic of interest to the user, even though the terminology employed in the document might be rather different than with which the user is familiar. A profile can also be used to guide a user’s search. A classic example would be a search for “Jaguar” returning different results for two users, one with an interest in cars, the other with an interest in natural history. Related to this is the idea of biasing search on the basis of the user’s task context. Whilst a user’s interests are relatively static, his task context changes with each session, or even in mid-session. Task context is therefore continuously being learned and continuously changing. In this case the response to a search for “Jaguar” might change depending on the information very recently viewed. Finally, profiles can be used to identify people and bring them together. Ideally we want here to know not just that someone reads documents about a certain topic, but also how frequently and what kind of document. Someone who frequently reads specialist documents on a subject is likely to be an expert. Identifying such a person to others who need advice is a valuable service. Someone who, perhaps less frequently, reads less “difficult” documents, or who has been identified as just beginning to take an
interest in the topic, might be identified as a novice and might wish to be put in touch with other such people to form a community of interest. This approach does depend not just on identifying the topic (or topics) describing a document, but also on being able to identify the intended readership. In general this would be based on the terminology employed in the document, analysed using machine learning techniques. In practice we may be dealing with very many documents, e.g. millions in a library database. Even the number of hits returned against a search may be extremely large. Visualisation techniques can be helpful to the user in understanding what knowledge exists, the relationship between the constituent documents, and which are most relevant to him. We can, for example, represent each document as a single dot, clustered and differentiated, e.g. by colour, in some way related to the ontology used. For an example of such an approach, see the DOPE project (Stuckenschmidt, 2004).
SEKT The previous section painted a picture of rich functionality made possible through the existence of a great deal of metadata conforming to a rich ontology. In practice it will often be the case that neither will exist to a sufficient degree. Proprietary information databases do come with metadata conforming to schemas, which can be viewed as simple ontologies. However, any particular academic or industrial library may use several such databases and the first problem will be to merge different schemas, or create mappings between them. Moreover, these schemas are often quite slowly changing and may not reflect the reality of a fast-moving subject, nor the structure of the interest of a particular user community. Finally, we may wish to extend our library by inclusion of relevant pages from the intranet. Such pages need to be automatically identified with topics. In any case, the document metadata are just that, describing the documents and saying nothing about the information entities (people, companies, roles etc) within the document. Moreover, ontologies change with time and must be managed and evolved. We need to be able to cope with the desire to return to a document created a year ago and described with an ontology subtly different from that in use today. These are exactly the kind of problems which the SEKT project is investigating. The goal of SEKT is to automate, or at least semi-automate, the creation and management of ontologies and metadata. To do this it uses its three core technologies of knowledge discovery[4], human language and ontologies; the last of which includes reasoning with ontologies. Using knowledge discovery and human language, our technology is able to learn new instances (e.g. of the classes Person and Company) and new relationships (e.g. that person A works for company B). In this way an initial knowledge base can be automatically extended. SEKT has three case studies and one of these, in BT, is employing SEKT technology within a digital library. In the other two case studies we are deploying the technology to aid decision making within the Spanish judiciary and to assist the effective sharing and reuse of knowledge within a large group of globally distributed IT consultants. Each of the case studies is intended to result in a working system, to be used by their own communities. Within SEKT their purpose is to test out the viability of the
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technology, both from the technical and user standpoints, and to feedback technical and user experiences into the development activity. The digital library in BT Our digital library case study did not start from scratch but builds on an existing digital library which has been in existence in BT since 1994. The core of the library comprises two databases, Inspec and ABI/Inform, which together contain a million documents from over 12,000 publications. The documents are, in fact, abstracts of around 200 words. In a significant number of cases the full text is also available for download. In other cases the user must obtain this from elsewhere, e.g. from another electronic source or as a paper copy from the British Library. The library also holds almost 1,000 management and business magazines on-line, plus a number of technical books. Currently, the user is able not only to search on a text string, but also to augment his search by using some of the available metadata, e.g. author. There is an alerting facility, whereby the user can define a search and then use this as the basis for regular alerts. These alerts occur regularly, when the databases are updated. There are also some predefined alerts to which a user can subscribe. The user is able to view which of his colleagues also subscribe to any given alert, and in this way the alerts can form the basis of communities of interest. For a further description of the functionality within the current BT digital library, see Alsmeyer and Owston (1998). A case study for SEKT Activity during the first two years of the project will lead to user trials in 2006, involving significant numbers of users. In the digital library case study these will be selected to include a large proportion of non-technical users, specifically individuals not trained in engineering and computer science. We believe that individuals with expertise in general management, marketing and human resources will be more representative of the general body of digital library users, and will be less forgiving of any deficiencies in the user interface. From these trials we hope to learn not just about technical viability but about what added functionality is valuable to users. We are fortunate in that the two library databases, Inspec and ABI, both come with extensive metadata. The schemas are, however, different and this in itself will be a test for the ontology mapping technology being developed in SEKT. In addition the database schemas are not sufficiently refined in some respects to reflect the interests of the users. We shall be applying knowledge discovery techniques to free text to identify new topics and locate them within the topic hierarchy. We see our digital library as increasingly integrated into the corporate intranet and into the Web generally. We want users to add web pages of interest to the library, so that they can be retrieved by others through the library interface. To do this we will be using knowledge discovery techniques to learn about the added pages, in particular what topics describe them. Encouraging people to share knowledge is notoriously difficult. We know, however, that many people habitually lose knowledge they have themselves stored, e.g. through bookmarks. By offering a framework within which knowledge can be stored and easily retrieved, we can at the same time create a mechanism and motivation for sharing knowledge.
Besides users adding knowledge, we are investigating the use of “crawling” techniques to search the web or corporate intranet to add new pages relevant to the subject matter of the digital library. However, the provenance of any particular knowledge item must be made clear to the user. Knowledge found on the web may not in general have the same authority as items in the library databases. Seeing the digital library within the wider context of the web can also help provide a better service to users. If the learning of a user’s profile is based solely on his use of the library, then the information on which that learning is based may be relatively limited. However, if the profile is influenced by the user’s web searching, then this is likely to provide a more accurate profile. This also overcomes the absence of a user profile when first using the digital library. Ontology engineering A well-designed ontology is essential for a successful semantic application. Within SEKT we are adopting a three-layer approach. An upper level ontology contains general classes. In fact these are a mixture of very general, e.g. Person, Role, Topic, TimeInterval, and classes which are more specific to the world of business, e.g. company, public company, media company. In all, the ontology currently contains around 250 classes. The middle layer of the ontology contains classes which are particular to knowledge management. Examples are UserProfile and Device. Of course, the boundary between this layer and the upper level ontology is necessarily arbitrary Finally, each of our three case studies has its own domain specific ontology. In the case of the digital library, this will contain classes relating to the specifics of the library, e.g. to the particular information sources available. A strength of the approach based on the use of an ontology language such as OWL, is the ability to accommodate distributed ontology creation activities, e.g. through defining equivalences. Nonetheless, where possible the creation of duplicate ontological classes should be avoided and where appropriate we make use of existing well-established ontologies, e.g. Dublin Core (http://dublincore.org/). Mention has been made on a number of occasions of the topic hierarchy. The approach adopted in creating this illustrates a difference in philosophy between designing an ontology and designing a conventional taxonomy. Within our upper level ontology there is a class, “Topic”. Each individual topic is an instance of this class. However, frequently a topic will be a subtopic of another topic, e.g. in the sense that a document “about” the former should also be regarded as being about the latter. Since topics are instances, not classes, we cannot use the inbuilt subclass relationship, but must define a new relationship subTopic. Such a relationship must be defined to be transitive, in the sense that if A is a subtopic of B and B is a subtopic of C, then A is also a subtopic of C. Thus we have a topic hierarchy. The taxonomic approach would be to simply define topics as classes and regard the documents as being members of the classes. There are a number of reasons for not adopting this approach. In the first place, it is epistemologically naı¨ve, in the sense that if we have a topic class, e.g. “nuclear physics”, then a document cannot properly be regarded as an element of such a class. Our approach has the virtue of flexibility. We can now use the Topic class in our description of a user profile. Specifically, we can have a relationship isInterestedIn with domain UserProfile and range Topic. Thus, in practical terms, we have related both documents and individuals to topics.
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We could have achieved the same effect, and avoiding epistemological inconsistency by defining classes such as “documents about nuclear physics”. We would then have a relationship isInterestedIn which would take as its object classes, such as “documents about nuclear physics”, rather than instances. This gives rise to certain computational difficulties and our approach is that of option 3 described in Noy (2004), where the various alternatives are discussed.
204 Revisiting the challenges We conclude by reviewing how semantic technology is meeting the challenges of digital library research. As we saw earlier, the US National Science Foundation identified four major themes (Lynch and Garcia-Molina, 1995): (1) Interoperability. Once we have overcome the basic difficulty of differing document formats, the next step is semantic interoperability, i.e. the ability to map between ontologies. Within SEKT this is one of the strands of our research. Within our digital library case study it is being used, e.g. to manage the disparity between the schemata associated with the Inspec and ABI databases mentioned above. (2) Description of objects and repositories. A theme of SEKT is the use of ontology languages, the languages which will be used to create these schemes. To an extent our work on ontology mapping and merging obviates the need to create single monolithic ontologies in any given domain. For simplicity we recommend reuse of common ontologies as far as possible, but use ontology mapping to overcome heterogeneity where necessary. (3) Collection management and organisation. SEKT is concerned with textual information. Non-textual objects having associated textual annotations can benefit from SEKT’s technology. Developing such semantic descriptions for multimedia objects is a significant field of research elsewhere, e.g. see (Hobson, 2004). (4) User interfaces and human-computer interaction. Ontologies can underpin how we navigate and visualise information. In SEKT our work on user interfaces embraces both aspects. Turning to the research agenda described by the NSF-Delos working group (Callan and Smeaton, 2003), semantic technology also has a role to play: . Modelling users. We have seen how knowledge discovery in particular can be used to automate, or at least semi-automate, the generation of user profiles. We intend in SEKT to develop the realistic approaches necessary. . Personalisation. We have talked about a user’s task context and his less rapidly changing interest profile. We need to consider both. We are also developing a facility within our case study for communities of users to have an interest profile, which will tend to evolve more slowly than that of individual users. . User interaction. The approach adopted in SEKT, based on the semantic description of documents and user interests, with the capability to do simple inferencing, offers a more precise recommendation capability than current text-based systems can achieve.
.
Social effects. The protection of privacy can be seen as a constraint on our systems. There is scope, through semantic descriptions of users and their communities, to make the definition of privacy more precise, and thereby minimise the constraints which it imposes.
The concluding words should be on evaluation. This was also identified by the working group, who stressed the need to take account of the individual user and the community of users. Evaluation is important in SEKT, because only through proper evaluation can we learn and improve. All aspects will be considered, from the technical to the organisational. Indeed, whilst there are significant technical challenges to be overcome, the real judgement on SEKT will depend on how we match the capabilities of the technology with the real needs of users. Notes 1. A project within the EU 6th Framework Programme, see: http://sekt.semanticweb.org 2. The DELOS network of excellence has existed within both the 5th and current 6th EU Framework Programmes. The reference here is to the former, whose web site is at http:// delos-noe.iei.pi.cnr.it/. The web site for the current DELOS network is at http://www.delos. info/ 3. The term “schema” is a misnomer here. In particular, RDF Schema does not bear the same relationship to RDF as XML Schema bears to XML. 4. The term “knowledge discovery” is used here to describe the application of machine learning, data mining and statistical techniques to uncover relationships. In particular, here we are concerned with discovering such relationships in text. References Alsmeyer, D. and Owston, F. (1998), Collaboration in Information Space, Proceedings of Online Information 98, Learned Information Europe, Sutton-on-Thames. Antoniou, G. and van Harmelen, F. (2004), A Semantic Web Primer, The MIT Press, Cambridge, MA. Callan, J. and Smeaton, A. (2003), “Personalisation and recommender systems in digital libraries”, Joint NSF-DELOS Working Group Report, May, available at: www.dli2.nsf.gov/ internationalprojects/working_group_reports/personalisation.html Gruber, T.R. (1993), “A translation approach to portable ontologies”, Knowledge Acquisition, Vol. 5 No. 2, available at: http://ksl-web.stanford.edu/KSL_Abstracts/KSL-92-71.html. Hobson, P. (2004), “Knowledge-based media analysis”, Proceedings of the European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology. Kalyanpur, A., Parsia, B. and Hendler, J. (2005), “A tool for working with web ontologies”, International Journal on Semantic Web and Information Systems, Vol. 1 No. 1, pp. 36-49. Lynch, C. and Garcia-Molina, H. (1995), ITTA Digital Libraries Workshop, available at: www-diglib.stanford.edu/diglib/pub/reports/iita-dlw/main.html Noy, N. (2004), “Representing classes as property values on the Semantic Web”, W3C Working Draft, July, http://www.w3.org/TR/2004/WD-swbp-classes-as-values-20040721/ Stuckenschmidt, H. (2004), et al., “Exploring large document repositories with RDF technology” The DOPE Project, IEEE Intelligent Systems, pp. 34-40.
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Towards personalization in digital libraries through ontologies
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Nu´ria Ferran Digital Library, Universitat Oberta de Catalunya (UOC), Barcelona, Spain, and
Enric Mor and Julia` Minguillo´n Computer Science and Multimedia Studies, Universitat Oberta de Catalunya (UOC), Barcelona, Spain Abstract Purpose – To describe a browsing and searching personalization system for digital libraries based on the use of ontologies for describing the relationships between all the elements which take part in a digital library scenario of use. Design/methodology/approach – Identification of all the desired functionalities and requirements that are necessary to fully integrate the use of a digital library in an e-learning environment, and the basic elements that are used to build the ontology that describes such scenario. Findings – The elements that determine the functionalities of the desired personalization system: first, the user’s profile, including navigational history and user preferences; and second, the information collected from the navigational behavior of the digital library users. Research limitations/implications – The ontology is not complete. In fact, the ontology in itself will evolve with the new apparition of desired functionalities and requirements of the personalization system. Practical implications – Such a personalization system will be very helpful to the users of a digital library to improve their experience of use. Originality/value – The use of ontologies promotes the integration of new services into existing ones, and the interoperability with other systems through the appropriate semantic web services. New system functionalities and requirements can be added by including the appropriate description into the ontology framework that defines the digital library scenario of use. Keywords Digital libraries, Information searches, Personal needs, Customization, Distance learning Paper type Research paper
Library Management Vol. 26 No. 4/5, 2005 pp. 206-217 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596062
Introduction Distance education is becoming one of the most attractive methods for incorporating all kinds of people into higher and university degree education levels, moving towards a “blended” technology approach deploying multiple technologies. The introduction of new technologies of information and communications with the intensive use of e-learning environments, as a virtual campus, for example, allows students to break through the barriers of space and time, and to design their own lifelong curricula, adapting it to their particular necessities and preferences, according to their possibilities as students, changing the usual way of both teaching and learning (Jonassen et al., 1995), setting up the foundations of e-learning environments (Rosenberg, 2001). The students of an e-learning environment have access to a predetermined repository of learning resources, which are part of the learning process designed by the
team of instructional designers and teachers for each course. But usually, these students might need more additional learning resources and documents to successfully follow the recommended learning itineraries, so there is the necessity of providing mechanisms for accessing such resources in a structured manner (Sicilia and Garcı´a, 2005). On the other hand, researchers and teachers in an e-learning environment are also usual “customers” of the services offered by an academic library, although under a completely different approach. These kinds of users have different experiences of use of the digital library than students, as their goals are clearly different, in both content and context aspects. All this richness needs to be captured somehow in order to better understand the way users of a digital library perform their navigation. Digital libraries must evolve in order to be proactive, more responsive to possible changes and to include new services such as personalization in order to increase user satisfaction and fidelity. Therefore, in an ideal scenario, the digital library should adapt to the specific characteristics of each user profile, but also to the particular necessities and preferences of each user, combining both user and profile level personalization capabilities. Personalization is one of the key factors which are directly related to user satisfaction (Riecken, 2000) and, therefore, linked to the failure or success of the performed activity, although it must be carefully introduced (Nielsen, 1998). Personalization has been shown useful in several areas such as e-commerce (Kasanoff, 2001), business-to-business companies (Colkin, 2001), and obviously reproduced in other environments such e-learning (Mor and Minguillo´n, 2004), for example. Regarding the library management field, there exist remarkable approaches such as the recommendation system for electronic journals of MyLibrary from Los Alamos National Laboratory 2.4 (http://lib-www.lanl.gov/) or MyOpenLibrary from the Open University (http://library.open.ac.uk/index.html); such personalization librarian initiatives are showing successful results as appears at the research being done about the user satisfaction (Sinha and Swearingen, 2001). In order to build such personalization system, several multidisciplinary aspects must be addressed: first, there are cognitive and behavioral aspects (Ford and Ford, 1993) that determine the way users perform searches and examine the obtained results. The “I’m Feeling Lucky” button in the Google search engine home page is a good example of such fact. Second, personalization issues must be addressed from a user-centered point of view, under the approach of human computer interaction, as it is well known (Schonberg et al., 2000) that most personalization systems fail, not because of the personalization system in itself, but in the interaction with the user and the way recommendations are presented. Third, there are technological and knowledge engineering aspects related to the way all this information is structured for both updating and querying purposes. In this paper we describe the set of desired functionalities and requirements of an ideal scenario for a digital library which includes personalization capabilities by means of ontologies. The use of ontologies for describing the possible scenarios of use in a digital library brings the possibility of predicting user requirements in advance and to offer personalized services ahead of expressed need. Ontologies are built using other sub-ontologies which describe the basic elements of the personalization system: users, digital resources, actions, navigational profiles, etc. This diversity of elements suggests that there is a need for further focus on the interoperability of objects which in turn requires well developed
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ontologies to describe the properties of both objects and individuals and the relationships between them (Brophy, 2004). This paper is structured as follows: the second section describes the basic functionalities of a digital library and the specific case of the UOC digital library, and the fundamental concepts of ontologies used in this paper. The third section defines the requirements and functionalities of a personalized digital library integrated in a virtual e-learning environment. All the elements defined as part of the ontology which gives support to the personalization system are described in the fourth section. Finally, conclusions and future research directions are outlined in the fifth section. Digital libraries and the case of the UOC As stated previously, one of the most important resources for supporting users in a distance e-learning environment is the possibility of accessing to a digital library, which allows the users to collect and organize the necessary information for achieving their particular goals. Furthermore, the search of information can be a learning but also an assessment activity by itself, so it is important to ensure and facilitate a proper use of the library. There are several terms being used interchangeably when we approach the concept of a library with digitized data and accessible remotely. Among these we can find hybrid library, digital library and virtual library. An informal definition of a digital library is “a managed collection of information, with associated services, where the information is stored in digital formats and accessible over a network” (Arms, 2002). The hybrid library provides electronic information sources too but also paper-based information. The hybrid should be considered as a model by itself not as a transitional phase from a conventional library to a digital one (Brophy, 2001). And finally the virtual library has been defined as the concept of a “remote access to the contents and services of libraries and other information resources, combining an on-site collection of current and heavily used materials in both print and electronic form, with an electronic network which provides access to, and delivery from, external worldwide library and commercial information and knowledge sources” (Gapen, 1993). Nowadays, we are finding new types of libraries coming up from long-term personal digital libraries, as well as digital libraries that serve specific organizations, educational needs, and cultural heritage and that vary in their reliability, authority and quality. Besides, the collections are becoming more heterogeneous in terms of their creators, content, media, and communities served. In addition, the user communities are becoming heterogeneous in terms of their interests, backgrounds, and skill levels, ranging from novices to experts in a specific subject area (Callan and Smeaton, 2003). This growing diversity has changed the initial focus of providing access to digital content and transforming the traditional services into digital ones to a new handicap where the next generation of libraries should be more proactive offering personalized information to their users taking in consideration each person individually (his or her goals, interests, level of education, etc.). While data and information are captured and represented in various digital formats, and rapidly proliferating, the techniques for accessing data and information are rudimentary and imprecise, mostly based on simple keyword indexes, relational queries, and/or low-level image or audio features (i.e., research results of the 1970s and 1980s). In the current context of explosive availability of data, there is a need for a knowledge discovery approach, based on both top-down knowledge creation (e.g.,
ontologies, subject headings, user modeling) and bottom-up automated knowledge extraction (e.g., data mining, text mining, web mining), promises to help transfer digital library from an institution of data and information to an institution of knowledge (Chen, 2003). The UOC virtual library The UOC Library was born in 1995 as a virtual academic library to support a virtual e-learning university model and, since then, provides online services and information resources both print and digital, owned by the library or by other libraries. Users can access the library from any computer and do not need to move in order to get any information resource. All authorized users are able to use remotely the exclusive content of the library as subscribed databases like the Electronic Management Research Library Database (Emerald) or the Web of Knowledge from ISI and benefit from the services of the library such as the Selective Diffusion of Information, Bibliographic Searches or Loans, all of them performed through the web. The UOC virtual library can be accessed in different ways. One of them is from the virtual campus where the user finds the whole content and services of the library. But the main focused (i.e., guided) entrance to the library can be found in the campus’s virtual classrooms where teachers and librarians bring a selection of the most interesting resources for every subject, for instance the learning material, recommended bibliography where each book is linked with the loan form, electronic articles, self-assessment exercises, a selection of internet resources, databases and electronic journals, e-books, exams from previous years, etc. This has been the first step for tailoring information for a very well defined community. The main objective of the library is to provide the students, but also lecturers, researchers and management staff, access to the information relevant for the fulfillment of their basic functions: learning, teaching, research and management. Several user profiles can be identified: undergraduate student, PhD student, teacher, learning process manager, among others. Each profile can be partially identified by the tasks related to the digital library that it performs. For example, students usually browse the digital library looking for documents related to activities such as exercises, exams and recommended articles and in very specific periods of time, when the delivery date of the exercise is due. On the other hand, teachers can navigate among the content of the library in order to mentor a student in doing his or her homework or providing content to the digital library associated to the virtual classroom. A final example could be the researchers who usually perform more focused searches during a larger period of time. Each of these users may have common goals in certain times but their knowledge, tasks, social activities and preferences are totally different. Ontologies and the Semantic Web Therefore, it is necessary to build a complete and complex structure for describing all the richness of the possible scenarios of use of the digital library and the relationships which can be established among all the participants. This can be achieved by means of ontologies and the use of the Semantic Web services in their appropriate forms (Sheth et al., 2005). An ontology is, taking the meaning adopted by the semantic web community, a formal description of a possible scenario or context; that is, what “exists” is what can be represented by an ontology. Formally, an ontology is the statement of a logical
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theory, but by “formal description” we also mean that it can be automatically queried and updated, as the main users of ontologies are (or should be) computers, not humans, in order to explicitly represent the objects, concepts and other entities that are assumed to exist in some context, altogether with the relationships that hold among them, although ontologies must be also human-readable. “Ontologies and taxonomies are, in functional terms, often used as synonyms. Computer scientists call hierarchies of structured vocabularies ontologies and librarians deploy the term taxonomy” (Adams, 2002). Nevertheless, ontologies also include a set of semantic rules which are used to infer knowledge from a structured hierarchy of information, giving to the complete structure a semantic meaning, not only syntactic (Gruber, 1995). Requiriments for an integrated, personalized digital library The web has become a very common tool for information browsing and searching, and the success of search engines such as Google or A9, for example, has facilitated the diffusion and access to repositories of digital documents. Despite that, one of the main problems of such search engines is that the generated results are not always of interest for the users performing the search, as these engines use a generalist approach based on several criteria which might not match the criteria of a specific user. On the other hand, several e-commerce web-based services, such as Amazon or e-Bay, for example, also provide browsing and searching services, but focused on categories. Both approaches can be combined to facilitate the way users browse the contents in search of information. Several techniques are used for guidance and for providing recommendations to users; among others, collaborative filtering (Herlocker et al., 2004) is one of the most successful ones. Briefly, collaborative filtering is selecting content based on the preferences of people with similar interests, basically by pooling and ranking informed opinions (or experiences of use) on any particular topic. That is to say, an automatic system collects information about user actions (explicit, such as voting or answering a question; or implicit, such as noticing which offered links are visited and which are not, and how much time) and determines the relative importance of each content by weighting all the collected information among the large amount of users. Both navigational techniques are also valid in a digital library scenario of use: simple searches starting from a single search term or advanced searches using multiple criteria, but also a recommendation system based on guided navigation through an ordered set of categories. The basic idea of this paper is that the efforts for finding a useful piece of information in a digital library carried out by an individual can be stored in a structured way and then shared for future users with similar necessities. Furthermore, if such information searching and browsing combines several web-based resources with different approaches (access mechanisms, query languages and interfaces, and so on), it is important to describe a common strategy (Sadeh and Walker, 2003) for minimizing the necessary efforts to fight against duplication (thus inconsistency) and source diversity. Identification of personalization system functionalities Two elements determine the functionalities of the desired personalization system: first, the user’s profile, including navigational history and user preferences; and second, the information collected from the navigational behavior of the digital library users. User profile should include all the information relevant to user: personal information, which
can be publicly made available by each user in order to facilitate the discovery of similar interests; and navigational history and behavior records, which will be used altogether with the personal information by the personalization system to build the set of recommendations that will help each user in browsing and searching the digital library. This information should help the user to improve his or her searches, by obtaining additional information when searching or browsing. It is remarkable to say that this information has been validated by the ontology, and that is not biased by any non-academic purpose of use (such as commercial-supported recommendations in Google or Amazon, for example). Table I shows the basic user profile attributes which are used to build the user model. For each attribute, it is shown whether it can be obtained or it must me given by the user, the relative importance for navigation and the recommendation system, and the user profiles which is more related to. For instance, the socio-demographical attribute may include information about the languages spoken or understood by the user, information that can be used as an additional filtering when browsing or searching for documents. This attribute has a medium importance with respect to personalization of navigational actions, and a low importance for the recommendation system. On the other hand, other attributes which express user preferences and interests have a high importance for both navigational and recommendation system actions. Other information about profiles such as information about factual/behavioral issues (Adomavicius and Tuzhilin, 1999) can be also included at this level. Two different behavior types can be identified, depending on the users’ navigation, exploratory navigation and goal-oriented navigation. The exploratory navigation can be mainly oriented to obtain a general vision of the available resources in the library. Depending on user profile, the exploratory navigation would have different implicit intentions. In the case of goal-oriented navigation, it is usually considered that the user is looking for a resource. These searches can be classified in different use cases. For example, in the situation of searching for an author, if the user is a student, the
Attributes Academic Register, Actual enrollment Academic Register, Previous enrollment Navigational history Navigational behavior and strategies Socio-demographical profile, General background Socio-demographical profile, Academic background Knowledge area Interests Preferences
Navigational Library recommendation support Is it level explicit? value level
Profiles concerned
No
High
High
Student
No
Low
High
Student
No No
High High
Medium Medium
All All
No
Medium
Low
All
No
Low
Medium
Student
Yes
High
High
Yes Yes
High High
High High
Teacher, Consultant, Researcher All All
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recommendations associated to search results should be oriented to the area of the course subjects, taking into account the navigation of other students and also the recommendations of the teachers. If the user is a researcher, recommendations should be oriented by different criteria depending on the searches that have been carried out by other investigators, or to the magazines, books and conferences where the searched author had published, understanding that the same magazine, conference or book might contain other interesting resources. Recommendations are generated using the knowledge extracted from the searching and browsing profiles of users with similar interests, knowledge integrated in the ontology such as course bibliography, or by following citations of similar documents, for example. Regarding the sources of information, using the library for accessing selected free Internet resources will be of particular interest, because the recommendation system ensures the users receive the opinions of a large set of experts (that is, the collaborative filtering system), therefore giving authenticity to such electronic information sources. On the other hand, regarding the library exclusive content, the user will get into the external databases (commercial) or internal databases (such as digital repositories or catalogues) in a transparent manner. Table II shows all the current information sources present in the digital library, and the basic actions that can be taken by the library users.
Navigation Catalogue Searching Browsing Browsing Browsing Command Digital collection Browsing Searching Command
Influence
Search physic and analogical documents (books, VHS, DVD, etc.) into the OPAC Navigate through author, subject, keyword, title, title course Look into the bibliographic register Look into the abstract and table of contents (for each document the DL scans the front cover, back cover, index) Loan command
Low Medium Medium Medium High
Navigate among the thematic classification nodes Search inside and electronic database Download, print, saving searches, sending searches by e-mail, create a Table of Contents (TOC) alert, add a journal to favorites, etc.
Medium Medium High
Medium
Command Command
Subscription to the News Services (for each study the librarians create a weekly newsletter with a news selection about the subject of the study. Users received it as an e-mail) Subscription to paper journal TOC Subscription to SDI (Selective Diffusion of Information)
High High
Library classroom Browsing Browsing Browsing Browsing
Look Look Look Look
Medium Medium Medium Medium
Subscription services Command
Table II. Information sources and basic user actions in the digital library
Actions
into into into into
the recommended bibliography the FAQs the didactic material exercises of other semesters
There are more possible actions that are currently being performed in the context of the digital library scenario of use but that are not integrated yet. For instance, teachers usually recommend a basic bibliography for any subject, which is supposed to be used by students for solving the course exercises. The use of an ontology could be also interesting to incorporate such new functionalities into the existing digital library, by describing the relationships between elements. In plain words: if a teacher defines one or more books as recommended bibliography for a given subject, students enrolled in such subject should be aware of those books when performing searches related to the subject. Discovering these new functionalities requires from the study of the current user behavior (taking into account the different user profiles) in the virtual e-learning environment, by means of classical HCI methodologies (Dix et al., 1998), involving users in the design of the new digital library. Then, these functionalities and requirements can be described as rules ranging from simple to complex statements within the ontology framework. For library managers, the creation of an ontology will help them to construct tailored libraries for each subject. Every library is built on the explicit recommendations from a teacher, but in an unstructured manner. With an ontology, these specialized libraries could be built from the use that previous students gave to that resources and new information could be added from the use of the library by experts. The ontology itself is composed of sub-ontologies. Thus, the digital resources, which are catalogued using the Dublin Core metadata or MARC format among other standards can be extended by means of an ontology to include additional information, automatically or in explicit way, such as their usage, user ratings and any other useful information that users might consider, such as summaries, enhanced taxonomies and keywords, for example. Privacy issues A very important aspect that cannot be ignored is the fact that users are always under control, in the sense that all taken actions are monitored and registered. This might seem a very invasive setup which harms user privacy and, therefore, undesirable. Nevertheless, there are several remarkable facts that need to be clarified: . users know in advance that, in a virtual e-learning environment (or any other web based environment), all actions are logged; . the recommendation system must be designed in a non-intrusive manner and be user-friendly, including the possibility of disconnecting it or minimizing its participation in the browsing or searching activities; and . the participation of each individual user in the final recommendation system is completely anonymous. Finally, it is also important to remark that the collected information is only used with personalization purposes, and it is not meant for commercial reasons, and that the library (a non-profit organization) will use the data rationally and in a transparent way. As usual, a tradeoff between personalization and privacy must be established. The more information the user reveals, the more personalized services he or she obtains. On the other hand, the browsing and searching history of each user is part of his or her private profile, and only the user can modify (delete) his or her history records in order to update his or her preferences and navigational profile, according to the directions given by the personalization system. Some parts of this profile can be made
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Elements of a digital library ontology First, it is important to clarify that we are not building an ontology for describing the contents of a digital library, but an ontology for describing the way users browse and search such contents, with the aim of building a personalization system based on accurate recommendations. Therefore, more than building a low-level ontology for describing a particular concept, we are trying to describe a complex scenario of use. Table III describes the basic steps (Denny, 2002) that should be followed for describing the richness of such complex scenario. As usual, these steps overlap and must be taken in a recursive way: depending on the results of the evaluation undertaken in the fifth step, several definitions in the second and next steps might be modified. In fact, the ontology in itself will evolve with the new apparition of desired functionalities and requirements. Acquiring domain knowledge and organizing the ontology, are where more efforts must be made to ensure a complete representation of the digital library scenario of use. As described above, Tables I and II outline the basic attributes and actions in the Step
Description
Acquire domain Assemble appropriate information knowledge Define all terms used in the domain to describe elements Check formality and consistency Organize the Design overall conceptual structure ontology Identify domain’s principal concrete concepts and properties Identify relationships among such concepts Minimize possible overlaps and inconsistencies Define actions performed between concepts Elaborate the ontology Consistency checking Validate the ontology
Table III. Basic steps for designing ontology
Resources/participants Expertise from library managers, computer scientists, usability experts
Definitions: user profiles digital resources navigational profiles learning activities Personalization system functionalities Add all the concepts, relations and individuals to Instances of previously defined concepts achieve a necessary level of detail Reuse information from the current environment as much as possible Ontology logging Reconcile syntactic, logical and semantic inconsistencies Involve automatic classification for defining new concepts and class relationships User tests Perform a final verification Commit the ontology in a real operative scenario Data analysis Ontology mining of use Compare the obtained results with the desired functionalities Detect all the possible mistakes or misuses of the real scenario
current digital library and e-learning environments, which should be used as basic pieces for building the ontology. Organization of the digital library ontology Therefore, the ontology is built from the intersection of the elements in Tables I and II, by describing all the interesting relationships for recommendation purposes. This is carried out by defining small micro-scenarios which reveal typical uses of the digital library and their impact on user profile and navigational history. For instance, suppose the following micro-scenario: John is a researcher working in his PhD in machine learning, as his profile says. He is working with Mary, his advisor, who got her PhD in the same field. Mary is also the advisor of other two students, Peter and Ann, which are also working in machine learning related subjects. When students search for scientific papers, the results are sorted depending on papers’ relative importance, according to the number of times each paper has been downloaded by other researchers (giving more importance to the other students which work with Ann) but also on whether such paper has been reviewed or not by any senior researcher (giving more importance to Ann’s opinion).With the appropriate tools and ontology definition languages, such statement can be translated into one or more rules that feed the recommendation system with the actions taken by all the users in the digital library. It is remarkable that the use of ontologies can be also extended to implement and transfer the concept of user profile and user navigational behavior to other digital libraries and databases, so when a digital library user leaves one service to connect into another one, the user profile (including preferences and navigational behavior) can be transferred from one database to another through the appropriate semantic web services, because all databases share a common domain of discourse that can be interpreted further by rules of inference and application logic. Regarding implementation issues, ontologies are usually described by means of one or more descriptive languages based on XML (W3C, 2004a). Basically, RDF (W3C, 2004b) is used for describing resources, while DAML þ OIL (W3C, 2004b) which is currently being evolved into the Web Ontology Language (OWL) standard (W3C, 2004c), is becoming the standard for describing ontologies and accessing resources through the web. The use of XML and description language standards ensures the interconnectivity with other existing ontologies and the possibility of upgrading the ontology for new requirements and functionalities. The widespread of XML for describing data (but also information and knowledge, with the help of ontologies) has made possible the apparition of new applications into classical areas of knowledge. Conclusions In this paper we have described the requisites of a personalization system which uses all information relevant to the process of searching and browsing a digital library to build a complete navigational profile for each user and its semantic description by means of an ontology. Then, all these profiles are then combined with the help of an ontology that establishes the possible relationships between all the elements present in a typical scenario of use in a digital library integrated in an e-learning environment. We have described the basic functionalities of the personalization system by means of use cases, and a methodology for building and ontology which describes the complete scenario of use. We have also identified the basic elements which are used to build such
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ontology: user profiles, navigational profiles, user actions and the relationships established between these elements which are used by the personalization system. Ontologies are a powerful tool for describing complex scenarios of use such as a digital library, where several concepts and relationships between these concepts can be identified and formally represented. The use of ontologies promotes the integration of new services into existing ones, and the interoperability with other systems through the appropriate semantic web services. New system functionalities and requirements can be added by including the appropriate description into the ontology framework that defines the digital library scenario of use. Current and further research in this subject include the integration of the digital library personalization services with other personalization mechanisms provided by the virtual campus, towards a unique and complete user model. The digital library should become, therefore, another piece of the e-learning environment fully integrated into the learning process. The inclusion of new concepts related to the temporal validity of the ontology instances (resources, users and so) and their relationships should be also addressed. Finally, the definition of a validation rating algorithm combining both automatic but also user explicit rating systems is also under consideration.
References Adams, K. (2002), “The Semantic Web: differentiating between taxonomies and ontologies”, Online, Vol. 26 No. 4, pp. 20-3. Adomavicius, G. and Tuzhilin, A. (1999), “Integrating user behavior and collaborative methods in recommender systems”, Proceedings of the CHI’99 Workshop “Interacting with recommender systems”, Pittsburgh, PA. Arms, W.Y. (2002), Digital Libraries, The MIT Press, Cambridge, MA. Brophy, P. (2001), The Library in the Twenty-First Century, Library Association Publishing, London. Brophy, P. (2004), “Networked learning and networked information: towards a theoretical basis for the development of integrated information environment”, Proceedings of the 4th Networked Learning Conference, Lancaster, UK, available at: www.shef.ac.uk/nlc2004/ Proceedings/Symposia/Symposium9/Brophy.htm Callan, J. and Smeaton, A. (2003), ”Personalization and recommender systems in digital libraries”, Joint NSF-EU DELOS Working Group Report, available at: www.dli2.nsf.gov/ internationalprojects/working_group_reports/personalisation.html Chen, H. (2003), “Towards building digital library as an institution of knowledge”, NSF Post Digital Library Futures Workshop, Chatham, MA, available at: www.sis.pitt.edu/ %7Edlwkshop/paper_chen.html Colkin, E. (2001), “Personalization tools dig deeper”, Information Week, No. 852, pp. 49-51. Denny, M. (2002), “Ontology building: a survey of editing tools”, xml.com, available at: www. xml.com/pub/a/2002/11/06/ontologies.html Dix, A., Finlay, J., Abowd, G. and Beale, R. (1998), Human-Computer Interaction, 2nd ed., Prentice Hall, Englewood Cliffs, NJ. Ford, N. and Ford, R. (1993), “Towards a cognitive theory of information accessing: an empirical study”, Information Processing & Management, Vol. 29 No. 5, pp. 569-85.
Gapen, D.K. (1993), “The virtual library: knowledge, society and the librarian”, in Saunders, L. (Ed.), The Virtual Library Visions and Realities, Meckler Corporation, Westport, CT, pp. 1-14. Gruber, T.R. (1995), “Toward principles for the design of ontologies used for knowledge sharing”, International Journal of Human-Computer Studies, Vol. 43, pp. 907-28. Herlocker, J.L., Konstan, J.A., Terveen, L.G. and Riedl, J.T. (2004), “Evaluating collaborative filtering recommender systems”, ACM Transactions on Information Systems, Vol. 22 No. 1, pp. 5-53. Jonassen, D., Davidson, M., Collins, M., Campbell, J. and Haag, B. (1995), “Constructivism and computer-mediated communication in distance education”, The American Journal of Distance Education, Vol. 9 No. 2, pp. 17-23. Kasanoff, B. (2001), Making it Personal, Perseus Publishing, Cambridge, MA. Lynch, C. (2003), “Digital library opportunities”, Journal of Academic Librarianship, Vol. 29 No. 5, pp. 286-9. Mor, E. and Minguillo´n, J. (2004), “E-learning Personalization based on Itineraries and long-term navigational behavior”, Proceedings of the 13th International World Wide Web Conference, Alternate Track Papers and Posters, pp. 264-265. Nielsen, J. (1998), “Personalization is over-rated”, Alertbox. available at: www.useit.com/ alertbox/981004.html Riecken, D. (2000), “Personalized views of personalization”, Communications of the ACM, Vol. 43 No. 8, pp. 27-8. Rosenberg, M.J.E-L.e. (2001), E-learning: Strategies for Delivering Knowledge in the Digital Age, McGraw-Hill, New York, NY. Sadeh, T. and Walker, J. (2003), “Library portals: toward the Semantic Web”, New Library World, Vol. 104 Nos. 1184/1185, pp. 11-19. Schonberg, E., Cofino, T., Hoch, R., Podlaseck, M. and Spraragen, S.L. (2000), “Measuring success”, Communications of the ACM, Vol. 43 No. 8, pp. 53-7. Sheth, A., Ramakrishnan, C. and Thomas, C. (2005), “Semantics for the Semantic Web: the implicit, the formal and the powerful”, International Journal on Semantic Web & Information Systems, Vol. 1 No. 1, pp. 1-18. Sicilia, M.A. and Garcı´a, E. (2005), “On the convergence of formal ontologies and standardized e-learning”, Journal of Distance Education Technologies, Vol. 3 No. 12, pp. 12-28. Sinha, R. and Swearingen, K. (2001), “Comparing recommendations made by online systems and friends”, Proceedings of the Second DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, available at: www.ercim. org/publication/ws-proceedings/DelNoe02/RashmiSinha.pdf W3C (2004a), eXtensible Markup Language (XML) 1.0 (Third Edition), available at: http://www. w3.org/TR/REC-xml/ W3C (2004b), Description Framework (RDF) Primer, available at: www.w3.org/TR/rdf-primer/ W3C (2004c), Ontology Language (OWL) Guide, available at: http://www.w3.org/TR/owl-guide/
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Integrating semantic metadata in P2P-based digital libraries Hao Ding
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Information Management Group, Norwegian University of Science and Technology, Trondheim, Norway Abstract Purpose – To propose methods for expressing semantics and operating semantics in largely distributed environment, such as peer-to-peer (P2P) based digital libraries (DLs) where heterogeneous schemas may exist and the relationships among them must be explicated for better performance in information searching. Design/methodology/approach – In conventional solutions, a mediator is adopted to create and maintain the matching between relevant terms such that distinct but relevant metadata schemas can be integrated according to the mapping relationships in the mediator. However, such solutions suffer some problems originated from the static matching in mediator. This paper proposes to use facts to express the relationships among heterogeneous schemas and conduct the reasoning dynamically by using inference engines. Findings – It is justified to use facts and inference engines to express and operate the semantics among heterogeneous but relevant information resources. The user can choose to convert only part of the XML document into facts if she can unpeel deeply nested XML tags. Additionally, it is possible for the user to manually edit (assert, update or retract) the facts as needed in the reasoning. Research limitations/implications – The study assumes that peers are clustered according to shared topics or interest. An exhaust evaluation has not been conducted. Practical implications – Each node can publish its schema to the involved peer community such that other peers can automatically discover the specific schema. A local matchmaking engine is adopted as well in order to automatically generate the relations between its own schema and the retrieved ones. Originality/value – This paper provides a framework for semantic data integration in P2P networks. Keywords Digital libraries, Internet, Information networks Paper type Research paper
Library Management Vol. 26 No. 4/5, 2005 pp. 218-229 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596071
Introduction Integrating heterogeneous data and information is a ubiquitous problem. Cooperative digital libraries (DLs), scientific communities, and average people with common interests are inclined to make their collections online as well as accessing the others’ information resources. With the exponential growth of online resources, it is easy for the user to get overwhelmed by the information flood. In order to search and process the information in a more efficient way, schemas are introduced to describe the basic structural information of the collections. Meanwhile, users prefer to have their own annotating schemas for their collections because they feel more accustomed to the semantic interpretations. Besides, since different DLs may be aimed at different users, domains or even topics, it is almost impossible to describe everything just in one huge schema. Instead, a large volume of heterogeneous schemas are created. Therefore, integrating data and information in such a large-scale and heterogeneous environment becomes a challenge.
The world wide web (WWW) and search engines (SEs) have taken dramatic success in providing an easy way for users to share distributed data and information. However, these approaches are impeded by an inherited limitation of supporting support keyword-based search only. Distributed databases (Ozsu and Valduriez, 1999) use catalogs to store fragmentation information. Each site has its own replica of the catalog and determines where to execute a query or parts of a conjunctive query. Such approaches are feasible if the number of nodes in the holistic system is not large. It is because of the fact that all the nodes of a distributed database management systems are under the control of a single authority. Data warehousing and data integration (Chawathe et al., 1997; Manolescu and Florescu, 2001) technologies are proposed to alleviate the problem as well. They rely on a two-tier mediator architecture, in which data sources are mapped to a global mediated schema that encompassed all available information. However, both of them are quite heavyweight because they impose a centralized mediating schema, which requires significant overheads in matchmaking and complicates the schema integration process as well. Peer-to-peer (P2P) based data management has received much attention recently as a more scalable and flexible architecture for sharing heterogeneous data. The major difference from previous approaches is that there is no central mediator node. One peer can harvest data from another peer if the mapping between the two schemas is defined. Hence, the user can be set free from concentrating on the centralized mediator node. But such architecture suffers in how to discover peers that hold relevant data. Currently, the search mechanisms in P2P network can be roughly classified into three categories. First, a centralized server maintains a global index for all participating peers and all queries are directed to the server, for Napster (www. napster.com) as such. Such solution suffers the same performance problem with exponential growth of the data. The second type of systems adopts some form of flooding (Gnutella, 2000) or gossiping (Aberer et al., 2003), which has known limitations on network utility. A distributed hash table (DHT)-based solution is the third type where the nodes are structurally organized by a hash function. The DHT-based mechanism is widely adopted to locate data sources in large scale systems (Galanis et al., 2003). One example is Chord (Stoica et al., 2001), which is scalable in locating data by adopting a hash table. However, metadata could not be located through a hash function in Chord and CAN, nor could it be treated as keywords. In this paper, we aim to alleviate the problem of semantic-based content search in federated DLs. Especially, we focus on the mechanisms in integrating heterogeneous schemas in P2P network. We propose to use the JXTA Search (Waterhouse, 2001) as an experimental platform. We argue that JXTA Search is appropriate for searches of distributed data sources that actively produce data, such as the news website or ordinary DL systems. The system builds indices on the queries a data source can answer and distributes them across JXTA hubs to which data sources connect. Thus, the way catalog information is distributed across the hubs is determined by where the data providers connect. It is anticipated that providers of similar topics will connect to the same hubs. In our case we implement it in a service discovery style such that the prototype can be easily extended to Web services. We assume in this paper the collections in independent and distributed nodes are XML collections. This premise
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originates from the fact that much textual content in DL is and will be encoded with XML document markup (Renear et al., 2002, 2003). However, it is clear that XML alone cannot meet the requirements of functionality and interoperability originally anticipated. The problem is that XML has not been designed for representing the fundamental semantic relationships among document components and features, even if it is able to identify a document’s meaningful structure. Coming back to the starting point, in order to support semantics-based searching, it is inevitable to consider schema and ontology mapping issues in the retrieval procedure. Herein, we differentiate for this paper the meaning of schema and ontology slightly. Schemas, which is to be carried out by document type definitions (DTD) or XML schema (Fallside et al., 2001), focus on the structural and syntax of an XML vocabulary adopted in collections, while ontologies (Gruber, 1993) are leveraged for basic facts and semantic relationships represented by the XML constructs. The next section introduces the P2P infrastructure we are to work with. This is followed by a section which provides an example to demonstrate the necessity of semantic markups in independent collections located in peers, and also indicates our intended approaches and tentative solutions. The penultimate section reviews current related works. Conclusions and future works are drawn in the final section. Architecture General architecture The features of exponentially increased data lie largely in the quantity and distribution. Gong (2001) discussed the advantages of adopting P2P technologies from the perspectives of three internet assets, namely, information, bandwidth and computing resources. P2P technologies can adopt a network-based computing style that neither excludes nor inherently depends on centralized control points. Additionally, the data and information resources on the web are also very dynamic, such as the collections in personal digital libraries, news website, product information and auctions. The conventional crawler-based approaches are ideal for static contents but not for the dynamic ones. Therefore, a complementary data sharing and integration mechanism is highly required for such applications. The nodes in the P2P network are comparably independent. However, considerable cooperations among nodes are also expected since relevant data and information may be scattered over distributed nodes. Figure 1 indicates that a peer can join several peer communities according to the topics he is interested, For example, he can instantly ask and join another peer community under the history topic, even if he is also involved in a nature science peer group. From the other perspective, if one will provide a bibliographic search service to the others, he can just send an advertisement to the public. In our project, we realize the communications among peers by adopting the JXTA platform which is to be briefly described in next section. The JXTA platform The prototype is based on the JXTA framework. JXTA is an Open Source project supported and managed by Sun Microsystems. In essence, JXTA is a network programming and computing platform that is designed to solve may P2P computing problems, such as interoperability, platform heterogeneity and ubiquitous
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Figure 1. JXTA architecture
computation. Generally, JXTA provides a core layer on top of which the service and applications are built (see Figure 2). In the core layer, a set of protocols are defined by one or more messages exchanged among participating peers; each message is in a predefined format. Currrently, six protocols are defined, namely the Peer Discovery Protocol, the Peer Resolver Protocol, the Peer Information Protocol, the Peer Membership Protocol, the Pipe Binding Protocol and the Endpoint Routing Protocol. On the basis of the core layer is the service layer where several JXTA services and diverse JXTA community services have been handled, such as indexing and searching. We can index all tags and texts in the XML fragments, typically the registration files in JXTA. All JXTA applications locate in the topmost layer which relies on the JXTA protocols and services to meet different needs pertaining to P2P networking. We are to design and implement the inference engine, rules portal, and automatic semantic mapping component in this layer. For further information about JXTA project we refer to (Oaks et al., 2002). Peer architecture This section describes in detail the structure of an average peer shown in Figure 3. Rule engine. On the left side, the rule engine is illustrated and the inner components are indicated as well. The rule engine contains an inference engine, a rule base, and a
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Figure 2. JXTA architecture
working memory. The purpose of introducing rule engine is to achieve more Inferencing capability in nailing down the implicit relationships among heterogeneous metadata terms. We integrate the JESS (Friedman-Hill, 2003), one of the Java based rule engines, into our prototype. The benefits of adopting rule engine in semantic search will be argued in the next section. The rule base contains all the rules the system knows. Practically, we present the mapping information in a set of rules and store them in the rule base. Meanwhile, ontologies, standard metadata information (i.e., Dublin Core (Dublin 2003)) and as well personal-defined (such as that in personal library) metadata elements must be referred to when mapping heterogeneous terms. The crucial part of the rule engine is obviously the inference engine, which controls the whole process of applying the rules to the working memory to obtain the outputs of the system. The working memory, which is transparent to average users, contains all the pieces of premises and conclusions of rules the system is working with. Local original sources (LOS). In order to simplify the problem, we assume that the collections in every peer are XML formatted. Yet in reality, many other collections may have different format, for instance, a travel agency may have the tourism information in the format of Excel, Word, PDF files or even emails. We will not consider these perspectives, though. This component stores the physical information to be shared in the P2P network. A peer can allocate self-defined tasks to its crawler to collect information on the network, which is especially common in personal libraries, where the owner may collect varied documents according to his preferences. Wrapper. We will not query over the XML collections directly, so we pre-process the XML collections, such as indexing according to the DTD or XML schema information. The user can also indicate the fields (tag names) which need to be indexed. Besides, wrapper can also be used to extract special information in LOS and transform results
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Figure 3. Individual peer structure
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to LPR, according to users’ requirements. The LPR is the real resource for searching and designed to support reasoning on the metadata records. Local peer repository (LPR). The information searching procedure is to be carried out in LPR, which also supports the browsing and navigating by local users. In strictly speaking, this component is not only a metadata annotated resource but also a hierarchical- and knowledge-based repository. Supported by the rule engine, a LPR may deduce over the terms in incoming queries and help retrieve the related metadata elements, and then extract the document information (indexed) and return them to the requester. We resort to WordNet (Miller et al., 1993) when we decide the relevant information, such as narrow, broad and related terms, since the volume of metadata elements information is executable when compared to the context information in average documents. Furthermore, we will allow the local user (administrator) to edit the mapping information as well. The Prote´ge´ editor is such an example since it can generate the OWL/RDF files after the edit. In general, LPR can not only let local users search information in a more natural way, but also improve the possibility for external peers to retrieve “appropriate” documents. User interaction (UI). UI allows users to browse and navigate information in their local repository. If the user has the administration right, he can publish some general information, such as content, topics, to the open peer community. Meanwhile, he can also send out requests to local or another peer community for requesting special information. (See Service Publishing and Resource Requesting in Figure 3.) As mentioned in a former section, if a peer is to provide searching functionality, he can just simply add advertisements into a LPR. Operating semantics The critical part in the whole system is obviously how to extract, express and exploit the semantic relationships in the heterogeneous metadata schemas. The extraction issue is easy to attack. Since there are many XML parsers available, the syntactic information, such as the tag name, contents between tags, can be obtained. The latter two issues are not though. Herein, we only discuss the expressing issue. As to how to express the relationships inside and among documents, many languages, such as RDF (Hayes, 2004), DAML þ OIL, and OWL (McGuinness and van Harmelen, 2004), are available. However, take the OWL inference engine for example, there is not a complete engine available currently to support the OWL FULL. And in order to meet the expressing requirements over heterogeneous schemas, we must have reasoning capabilities to handle the varied relationships among them. With such premise, we think of another alternative of implementing a function to convert an XML document (which is about metadata information) into a series of “expert system” facts. The user can choose to convert only part of the XML document into facts. The user can dynamically unpeel a complicated set of deeply nested XML tags. Furthermore, user can even manually edit (assert, update or retract) the facts as needed by the reasoning. To demonstrate the idea of this mechanism, we adopt a working example as follows. Example: semantic markup We use the term ”semantic markup” in this paper to clarify it from many interesting works in ”semantic annotation”, which focuses on identifying the semantics of textual or multimedia streams. Herein, we concentrate on extracting the semantics embedded
in document markups as well as making it “processible” to both of the human and machine. Markup semantics are defined in (Renear et al., 2002) as applying knowledge representation technologies to the problem of making the structures, relationships and properties explicit. Let us consider the following fragment of an XML-tagged document from Financial Times (FT) Collection in TREC 4 (TREC, 2002). , DOC . ,DOCNO . FT911-376 , /DOCNO. ,HEADLINE . FT 13 MAY 91/Survey of Cardiff (2): Selling on the road – The financial sector ,/HEADLINE . ,BYLINE . By ANTHONY MORETON, /BYLINE . ,TEXT . Although the day-long event was one of a series that will. . .(Omitted) , /TEXT . ,PUB . The Financial Times , /PUB . ,PAGE . London Page 16 Photograph The Bank of Wales was set up in 1972, and moved to its new building in September (Omitted). ,/PAGE . , /DOC .
Expressing semantics From the literal in the fragment, it is moderate for readers to deduce the meaning of tag names. For instances, “DOCNO” can be roughly regarded as “Identifier” in the Dublin Core, “BYLINE” is subsumed by “Creator”, and “Page” as “Relation”. Two tags are odd. The “HEADLINE” combines the “Date” and “Title” information as are in Dublin Core, We are not to discuss it in this paper because of the topic and just roughly regard it equal to “Title”. “TEXT” is another tag which does not have correspondent in Dublin Core. So, we just remain it unchanged. We illustrate the relationships between FT metadata and Dublin Core metadata as follows. In Figure 4, the lower part illustrates the syntactic view of this XML fragment; and the relationships against Dublin Core are indicated by the dotted lines in the upper part. We have converted the mapping information into the following rules: (defrule RuleFTDC “Indicating semantic relationships between FT and DC metadata” (Tag (DocumentName ?d) (TagName “DOCNO”) (DocNoName ?docno)) (Tag (DocumentName ?d) (TagName “HEADLINE”) (HeadlineNmae ?headline)) (Tag (DocumentName ?d) (TagName “BYLINE”) (BylineName ?byline)) (Tag (DocumentName ?d) (TagName “PAGE”) PageName ?pagename) (Attribute (DocumentName ?d) (CollectionName “FT”)) ¼. (assert (EqualList (DocumentName ?d) (DCIdentifier ?docno) (DCTitle ?headline) (DCCreator ?byline) (DCRelation ?pagename))) )
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Figure 4. The Syntax Tree of Doc. ”FT911-376” and its enhancement with semantics
Interoperation among various applications in the Semantic Web demands the fulfillment of some properties. We argue that our approach currently is suitable for handling inferencing. First, we evaluate OWL Lite to study its inferencing capabilities in a general way (see Table I), since OWL FULL is too complicated and there is no complete inference engine for it so far. As we can see that OWL Lite does not support symmetry, inverse and composition axioms, while in JESS, we can just explicitly indicate such relationships in the rule base, without bothered by the syntax limitation in OWL Lite. Take one example, we present an inverse relationship which can indicate that in one XML fragment, one author may have several publications, while in another XML fragment, author tag may be nested in the publication tag since a publication should have at least one author’s name.
OWL Lite Semantic Web components Data changes Relationships desc. Concept and relation hierarchy Axioms (symmetry) Axioms (inverse) Axioms (composition) Resources Table I. Rough evaluation of OWL Lite
Logic theory Inference mechanism
Yes Yes Yes No No (but OWL Full supports it) No (but OWL Full supports it) Yes Description logic Requires additional formalisms for XML and RDF to define their semantics or specify axioms and constraints
(defrule RuleInverse “Indicating the inverse relationship between author and publication” (Tag (Document1Name ?d1) (TagName “author”) (AuthorName ?authorname)) (Tag (Document1Name ?d1) (TagName “publication”) (ParentID “author”) (PublicationNmae ?publication)) (Tag (Document2Name ?d2) (TagName “author”) (ParentID “publication”) (AuthorName ?authorname)) (Tag (Document2Name ?d2) (TagName “publication”) (PublicationNmae ?publication)) ¼. (assert (InverseAxiom (Document1Name ?d1) (Document2Name ?d2) (DCCreator ?authorname) (DCPublisher ?publication))) )
Related work Information searching over P2P network is not a new scenario, but the semantic searching over such network is. The conventional P2P networked realized searching by doing string matching between the query and the names of files on the hosts’ library. Consequently searches are restricted to strings that can be contained in a filename. Perhaps the earliest metadata based information searching over P2P network is work done by Sumeet Thadani (Thadani, 2001), which proposed a technique for allowing richer querying on metadata associated with files in host’s library. Query requests will encode the richer queries and responses will contains results based on the rich query searches in addition to the regular results. The proposed scheme will ensure that the protocol continues to work with older clients, which do not understand the embedded rich queries. However, such approaches do not touch the heterogeneity issue in the metadata schemas. Edutella (Nejdl et al., 2002) is an excellent work which provides an RDF-based infrastructure for exchanging metadata in P2P applications. The Edutella Query Services (QEL (Nejdl et al., 2002)) is intended to be a standardized query exchange mechanism for RDF metadata stored in distributed RDF repositories. The Edutella project focuses on the education community. (Brase et al., 2003) is an extended work based on Edutella platform, it adopts TRIPLE language (Sintek and Decker, 2002) to express suitable rules and axioms, and extends the purely syntactic definition given in the learning objects metadata (LOM) specifications by such axioms and inference rules so as to avoid redundant metadata annotation and derive additional metadata from existing ones as well. Halevy et.al. consider the problem of schema mediation in a peer data management system (PDMS). They propose a flexible language for mediating between peer schemas, which extends known data integration formalisms to more complex architecture. They realized algorithms for chaining through multiple peer mappings in order to locate data relevant to a query. Such approach work well when the collections on varied peers are rich and similar, and there is still a problem in retrieving information from peers with small collections. Our work differs in several ways. First, our focus is how to integrate the semantic reasoning functionality into the conventional information retrieval procedure against
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P2P network. Second, we use rule engine to do the reasoning over XML files by predefining the mapping rules in rule base. Third, the administrator of local peer has more flexibility in creating its own rules and facts according to the local peer’s preferences and characteristics, which is crucial to the library systems. Lakshmanan and Sadri (2003) proposed to enrich local sources with semantic declarations so as to enable interoperability. Rather than determining a commonly agreed upon global schema (Global as View – GAV) or translating schemas among local schemas (Local as View – LAV), the approach adopts these declarations to expose the semantics of the information content of sources by mapping the concepts present therein to a common and application specific vocabulary. All participating sources must map its data to the standard ontologies that already exists, though. Conclusions and future work In this paper, we presented a framework for semantic data integration against P2P network where many heterogeneous metadata schemas exist. We discuss the general P2P architecture, especially under the digital library application scenario. A detailed structure of a specific peer has been described. We assumed that all of the collections in the P2P network are XML formatted. A proposed mechanism in dynamically expressing the implicit relationships among metadata elements are described as well. Currently, we are working on implementing an adaptive information retrieval method which can modulate the searching according to the results generated from the rule engine. Likewise, consideration on the query features of general P2P networks is highly required in order to couple with the IR component. Much research has recently focused on tackling some challenges in P2P network with respect to autonomous pooling resources (e.g., files, storage, and compute cycles), high availability and fault-tolerance, and self-organization of peers (Daswani et al., 2003). We recognize the strong need for such researches, but the development of such tasks is not part of our work. One critical task in our future work is to evaluate the approach of rule-based information searching in P2P network. The whole and concrete evaluation will be carried out by comparing with simple keyword-based information searching in P2P networks. References Aberer, K., Cudre-Mauroux, P. and Hauswirth, M. (2003), “The chatty web: emergent semantics through gossiping”, paper presented at the International World Wide Web Conference, Budapest. Brase, J., Nejdl, W., Painter, M., Sintek, M. and Thaden, U. (2003), “Intelligently authoring metadata for a semantic web peer-to-peer environment”, available at: www.kbs. uni-hannover.de/Arbeiten/Publikationen/2003/ISWC_long.pdf Chawathe, S., Garcia-Molina, H., Hammer, J., Ireland, K., Papakonstantinou, Y., Ullman, J.D. and Widom, J. (1997), “TheTSIMMIS.project: integration of heterogeneous information sources”, Journal of Intelligent Information System, Vol. 8 No. 2, pp. 117-32. Daswani, N., Garcia-Molina, H. and Yang, B. (2003), “Open problems in data-sharing peer-to-peer systems”. paper presented at the International Conference on Database Theory (ICDT), Siena. Dublin Core (2003), “Dublin Core metadata initiative”, available at: www.dublincore.org
Fallside, D.C., Thompson, H.S., Beech, D., Maloney, M. and Mendelsohn, N. (2001), “W3C architecture domain – XML Schema”, available at: www.w3.org/XML/Schema Friedman-Hill, E. (2003), JESS in Action: Rule-based Systems in Java, Manning Publications, Greenwich, CT. Galanis, L., Wang, Y. and DeWitt, S.R.J. (2003), “Locating data sources in large distributed systems”, paper presented at the 29th VLDB Conference, Berlin. Gnutella (2000), “Lime Wire LLC 4.0”, available at: www.limewire.com/ Gong, L. (2001), “Industry report: Jxta: a network programming”, IEEE, Vol. 5 No. 3. Gruber, T. (1993), “What is ontology?”, available at: www-ksl.stanford.edu/kst/what-is-anontology.html Hayes, P. (2004), “RDF semantics”, available at: http://www.w3.org/TR/rdf-mt Lakshmanan, L.V. and Sadri, F. (2003), “Interoperability on XML data”, paper presented at the 2nd International Semantic Web Conference, Sanibel Island, FL. McGuinness, D.L. and van Harmelen, F. (2004), “OWL Web ontology language overview”, available at: www.w3.org/TR/owl-features/ Manolescu, I. and Florescu, D. (2001), “Answering XML queries on heterogeneous data sources”, VLDB Proceedings, pp. 241-50. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D. and Miller, K. (1993), Introduction to WordNet: An On-line Lexical Database, Cognitive Science Laboratory, Princeton University, Princeton, NJ. Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve, A., Nilsson, M., Palmer, M. and Risch, T. (2002), “Edutella: a P-2-P networking infrastructure based on RDF”, paper presented at the International World Wide Web Conference, Honolulu, HI. Oaks, S., Traversat, B. and Gong, L. (2002), JXTA in a Nutshell, O’Reilly & Associates, Sebastopol, CA. Ozsu, M.T. and Valduriez, P. (1999), Principles of Distributed Database Systems, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ. Renear, A., Dubin, D., Sperberg-McQueen, C. and Huitfeldt, C. (2002), “Towards a semantics for xml markup”, Proceedings of the 2002 ACM Symposium on Document Engineering, Mclean, Virginia, USA, pp. 119-126 Renear, A., Dubin, D., Sperberg-McQueen, C. and Huitfeldt, C. (2003), “XML semantics and digital libraries”, ACM/IEEE Joint Conference on Digital Libraries (JCDL), Houston, Texas, USA, pp. 303-305. Sintek, M. and Decker, S. (2002), “Triple – a query, inference, and transformation language for the Semantic Web”, Proceedings of the 1st International Semantic Web Conference. Stoica, I., Morris, R., Kaashoek, F. and Balakrishnan, H. (2001), “Chold: a scalable peer-to-peer lookup service for internet applications”, SIGCOMM, San Diego, CA. Thadani, S. (2001), “Meta information searches on the gnutella network”, available at: http:// rfc-gnutella.sourceforge.net/Proposals/MetaData/meta_information_searches.htm. TREC (2002), NIST TREC document database: Disk 4 – Financial Times (1992-1994), available at: www.nist.gov/srd/nistsd22.htm Waterhouse, S. (2001), “JXTA search: distributed search for distributed networks. White paper”, available at: http://search.jxta.org
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Building a distributed digital library for natural disasters metadata with grid services and RDF Wei Xing and Marios D. Dikaiakos Department of Computer Science, University of Cyprus, Nicosia, Cyprus
Hua Yang Xian Institute of Post and Telecommunication, Nicosia, Cyprus, and
Angelos Sphyris and George Eftichidis Algosystems SA, Kalithea, Greece Abstract Purpose – This paper aims to describe the main challenges of identifying and accessing useful information and knowledge about natural hazards and disasters results. The paper presents a grid-based digital library system designed to address the challenges. Design/methodology/approach – The need to organize and publish metadata about European research results in the field of natural disasters has been met with the help of two innovative technologies: the Open Grid Service Architecture (OGSA) and the Resource Description Framework (RDF). OGSA provides a common platform for sharing distributed metadata securely. RDF facilitates the creation and exchange of metadata. Findings – Using grid technology allows the RDF metadata of European research results in the field of natural disasters to be shared securely and effectively in a heterogeneous network environment. Originality/value – A metadata approach is proposed for the extraction of the metadata, and their distribution to third parties in batch, and their sharing with other applications can be a quickly process. Furthermore, a method is set out to describe metadata in a common and open format, which can become a widely accepted standard; the existence of a common standard enables the metadata storage in different platforms while supporting the capability of distributed queries across different metadata databases, the integration of metadata extracted from different sources, etc. It can be used for the general-purpose search engines. Keywords Digital libraries, Data analysis, Natural disasters Paper type Research paper
Library Management Vol. 26 No. 4/5, 2005 pp. 230-245 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596080
Introduction Research in natural hazards focuses on unraveling and understanding processes, comprehensive risk assessment, forecasting and risk management and mitigation. Advances have been made in seismic research, forest fires, landslides, floods, volcanic hazards, avalanches and technological hazards, particularly with the development of improved models and technologies for hazard forecasting, risk assessment and mitigation. Research projects focusing on natural hazards and disasters produce results in the form of explicit or tacit knowledge represented by reports, project deliverables, data-sets derived from field work, interesting training and dissemination material, etc. These artifacts are usually published and described on web sites
maintained by project partners during the duration of the respective projects. Following project completion, however, project teams dissolve and web-site maintenance and support gradually fade out. Hence, general-purpose search engines are used to search for past-project results. Nevertheless, search-engine query results provide large numbers of unrelated links. Furthermore, hyperlinks pointing to potentially useful material do not come with references or additional links to adequate information describing the “value” of identified resources. Consequently, identifying and accessing useful information and knowledge becomes very difficult. Effectively, valuable knowledge is lost and it is practically impossible to find and take advantage of it. To address this problem, the Directorate General for Research of the European Union undertook the initiative to establish the European Mediterranean Disaster Information Network (EU-MEDIN, 2003). EU-MEDIN’s goal is to foster coordinated and increased access to data and expert know-how before, during, and after a disaster strikes. The availability of reliable and timely information could contribute to our knowledge for reducing impacts of hazards and risks and bring about improved disaster preparedness in Europe in the near future. As the first step for the deployment of EU-MEDIN, the EU commissioned Algosystems SA with the development of a thematic web portal to support the storage and retrieval of metadata pertaining to results of research in natural disasters. Project-related metadata can be inserted via a Web interface to a back-end database (EU-MEDIN, 2003). Interested researchers can use the EU-MEDIN portal to query the database and search for project-artifacts. This approach, however, encodes and maintains the metadata in the platform-specific format of the particular database system chosen for the development of the EU-MEDIN portal. Therefore, the extraction of the metadata, their distribution to third parties in batch, and their sharing with other applications can be a lengthy process. Furthermore, there is a need to describe metadata in a common and open format, which can become a widely accepted standard; the existence of a common standard enables the metadata storage in different platforms while supporting the capability of distributed queries across different metadata databases, the integration of metadata extracted from different sources, etc. In this paper, we present gDisDL, a grid-based digital library system designed to address some of the problems mentioned above. Our approach comprises: . A schema for describing project-related metadata in a platform-independent form, using the Resource Description Framework (RDF). RDF is a general framework for describing metadata of Internet resources and for processing this metadata; it is a standard of World Wide Web Consortium (W3C). RDF supports the interoperability between applications that exchange machine-understandable information on the web. . A digital library system enabling the collection and storage of RDF-encoded metadata in distributed repositories, and the retrieval thereof from remote sites. This library is implemented as a Grid-service architecture comprised of a set of grid services, which allow the storage, management, and query of RDF metadata in a secure and distributed manner. To develop the library we use the Globus Toolkit 3 (Sotomayor, 2003) for programming grid services and the Jena toolkit (JENA, 2003) for handling RDF data.
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A set of graphical-user interfaces developed in Java to enable authorized end-users to create RDF metadata for natural-disaster research artifacts and to conduct keyword-based searches in RDF repositories.
The remainder of this paper is organized as follows. In the second section, we give a short overview of the technologies that we adopted to design and build gDisDL: in particular, the RDF, the Jena RDF toolkit, and the Open Grid Service Architecture (OGSA). In the third section, we present an overview of the EU-MEDIN RDF schema. The design challenges and architecture of the gDisDL system are presented in the fourth section. In the fifth section, we give an overview of some interesting implementation issues. The final section concludes our paper. Background RDF RDF is a language used mainly for representing information about resources on the world wide web (Manola and Miller, 2003). In particular, it is intended for representing metadata about documents or other entities (e.g, web resources, software, publications, reports, image files etc), such as title, author, modification date, copyright, and licensing information. Although originally intended to be used on web resources, RDF is capable of representing information about things that can be identified on the Web, even when they cannot be directly retrieved from the web (Manola and Miller, 2003). This capability makes RDF an appropriate metadata schema language for describing information related to the various results and outcomes of natural-disaster research. RDF is designed for situations in which information needs to be processed by applications, rather than only being displayed to people. RDF provides a common framework for expressing information and can thus be exchanged between applications without loss of meaning. Since it is a common framework, application designers can leverage the availability of common RDF parsers and processing tools. The ability to exchange information between different applications means that the information can be made available to applications other than those for which it was originally created. The way, RDF is able to describe various resources (not restricting the user to the description of web resources) and represent different kinds of information into one unique data model, makes it a good candidate for our purpose, since the data that the users will be dealing with, can be of any format or type. Jena Jena is a Java toolkit for building Semantic Web applications (JENA, 2003). The Jena Java RDF API is developed by HP Labs for creating and manipulating RDF metadata. It comprises: . “Another RDF Parser” (ARP), a streaming parser suitable for validating the syntax of large RDF documents. ARP complies with the latest recommendations of the RDF Core WG, as well as with standards and recommendations, such as XML (Bray et al., 2004), DAML (Connolly et al., 2001), and URI (Berners-Lee et al., 1998). . A persistence subsystem, which provides persistence for RDF metadata through the use of a back-end database engine. It supports RDQL queries.
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The RDF query language (RDQL), which is an implementation of an SQL-like query language for RDF. Jena generates RDQL queries dynamically and executes RDQL queries over RDF data stored in the relational persistence store. A reasoning subsystem, which includes a generic rule-based inference engine, together with configured rule sets for RDFS and for the OWL/Lite (Patel-Schneider et al., 2004). An ontology API, which supports OWL, DAML þ OIL and RDFS.
Open Grid Service Architecture The grid supports the sharing and coordinated use of diverse resources in dynamic, distributed “virtual organizations” (VOs) (Foster et al., 2001). The Open Grid Services Architecture (OGSA) is a service-oriented grid computing architecture, which is an extensible set of grid services that may be aggregated in various ways to meet the needs of VOs (Foster et al., 2002). OGSA defines uniform grid service semantics and standard mechanisms for creating, naming, and discovering grid services. Web service technologies, such as XML, SOAP, WSDL, UDDI, etc., are adopted to build up the grid services infrastructure. A grid service is a web service that provides a set of well-defined interfaces and follows specific conventions (Foster et al., 2001). The interface and behaviors of all grid services are described by the Grid Web Service Description Language (GWSDL) (Sotomayor, 2003). Furthermore, the Open Grid Service Infrastructure (OGSI) gives a formal and technical specification of the Grid service, and the Globus Toolkit 3 (GT3) offers a programming environment for implementing Grid services (Tuecke, 2002). Metadata elicitation As mentioned earlier, the goal of the gDisDL system is to support the storage and retrieval of metadata (i.e., structured data about data) that pertain to a variety of results derived from research in natural disasters, such as earthquakes, floods, forest fires, industrial hazards, landslides, and volcano eruptions. To this end, we need a metadata schema that would provide a useful, adequate representation of project “resources” The term “resource” here is used to collectively refer to results of projects, as well as projects themselves. Furthermore, we need to define this schema in a common and open format, which will: . promote the standardization of natural disaster metadata, while at the same time allowing future extensions; . enable the storage of metadata in different platforms according to this common, standard schema; and . support the interoperability of different metadata repositories; in particular the specification of queries and the execution thereof upon different metadata databases. To develop such as schema, the Coordinator and Steering-group members of the EU-MEDIN project (EU-MEDIN, 2003) undertook the effort of reviewing several projects in natural-disaster research with the aim of describing each type of resource by means of an EU-MEDIN metadata schema. One of the first steps in that effort was
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the identification of the entire set of possible types of results that could arise during the course of a project, and for which it would be interesting to create respective metadata records in a database. Currently, the following types of resource are included in the EU-MEDIN metadata collection: project, report/deliverable, journal paper, other scientific paper, student dissertation, press article, media presentation, book, event, hardware, software, web site, spatial digital dataset, experimental dataset (laboratory), experimental dataset (field), and unclassified activity. An important element of this schema is the set of subjects of a resource. The subjects of a resource provide a classification for the resource, which is understandable quickly and easily, in very much the same way as the words drama/historical would indicate the nature of a film or a TV program in a printed listing. For each and every resource in the database, a value is stored for its set of subjects. The subjects of a resource are in effect, two-dimensional, that is, they are composed of two values. The first of these is termed “primary subject” whereas the second is termed “secondary subject”. In this setting, the primary subject indicates a type of risk with which the resource is associated (e.g. volcanoes, earthquakes), whereas the secondary subject indicates an aspect of such a risk. The possible values for the primary and secondary subjects were derived through a consensus process involving the EU-MEDIN project Steering Group and officers at the Directorate General of Research of the European Commission. The primary subjects of a resource are drawn from the following set of values: “Forest Fires”, “Desertification”, “Droughts”, “Floods”, “Storms”, “Avalanches”, “Landslides”, “Earthquakes”, “Seismic Engineering”, “Volcanoes”, “Industrial Hazards”, “Other Risk”. The secondary subjects of a resource are drawn from the set: “Hazard Assessment”, “Forecasting and Monitoring”, “Modeling and GIS”, “Earth Observation/RS”, “Vulnerability Assessment”, “Risk Analysis and Mapping”, “Damage Assessment/Restoration”, “Management/Mitigation”, “Crisis Management/Intervention”, and “Other Aspect”. The primary and secondary subjects value for a resource may be any series of ordered pairs of values drawn from the above two sets. If a user selects a pair containing “Other Risk” or “Other Aspect,’’ then a mechanism should be provided for them to specify just what this “other” selection signifies. We adopted the RDF Schema (Brickley and Guha, 2000) to describe the identified set of resources classes, properties, and values for the EU-MEDIN resources. Following the classification of distinct resources identified in the EU-MEDIN project, we introduced an RDF Schema with 16 classes (“EC project”, “Report/deliverable”, “Journal paper”, etc), and defined their properties and the relationships between class instances accordingly. The resulting schema is named EU-MEDIN RDF schema and represents our proposed metadata standard for natural-disaster research resources. This schema was based on two already existing schemas that have been established through other initiatives: the Dublin Core Metadata Element Set (DCMES) (Dublin Core, 2003) and the Federal Geographic Data Commission Content Standard for Digital Geospatial Metadata (FGDC-CSDGM). The EU-MEDIN schema relies more on DCMES than it does on FGDC-CSDGM. Figure 1 shows the class hierarchy of the EU-MEDIN RDF schema. Below, we give an example extracted from the EU-MEDIN RDF schema. This excerpt of our schema includes two classes, Press article, and EC Project, and three properties, author, belongTo and name. Using these classes and properties, we can
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Figure 1. Class hierarchy for the EU-MEDIN RDF schema
describe the following piece of knowledge in RDF: “John Smith wrote a Paper, which belongs to FloodSim project. The paper’s title is ‘Flood Simulation on the Grid’”. Figure 2 shows part of the schema definition and the RDF description of the example, presented as an RDF graph. The RDF data can also be represented and stored physically as a set of RDF triples in N3 (Berners-Lee, 2000): @prefix:,http://www.eu-medin.com/publication/floodG# . @prefix dlib:,http://www.eu-medin.com/2003/02/schema# . @prefix rdf:,http://www.w3.org/1999/02/22-rdf-syntax-ns# .
Figure 2. Example of an EU-MEDIN RDF schema
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236 gDisDL system design gDisDL system is a grid service-oriented system designed to collect, process, store, and query the RDF metadata encoded according to the RDF EU-MEDIN schema. As shown in Figure 3, the gDisDL system is comprised of a number of geographically distributed gDisDL nodes and a UDDI registry. Each node consists of a Data Aggregator and a Searcher. The Data Aggregator collects, validates, and encodes metadata in RDF; the Searcher is designed for querying RDF metadata. The functions of the components of the gDisDL system will be provided through two gDisDL grid services: a Data Aggregator grid service and a Searcher grid service. The UDDI server is used for publishing and discovering information about the gDisDL grid services (UDDI version 3.02). The Grid Authentication service is a credential service of the Grid Security Infrastructure (GSI) to protect gDisDL grid services invoking by unauthorized clients (Foster et al., 1998). Finally, the Editor and the Searcher GUI are grid service clients for the Data Aggregator and the Searcher grid services, enabling users to interact easily with gDisDL through a graphical-user interface. Design goals The main design issue of the gDisDL system is that the distributed RDF metadata should be shared efficiently and securely in an ad-hoc, dynamic basis. To address this
Figure 3. The architecture of the gDisDL system
challenge, we organize the distributed gDisDL nodes and their RDF metadata using the scalable Virtual Organization (VO) mechanism of the grid (Foster et al., 2001). To this end, we have designed gDisDL to comply with the Open Grid Service Infrastructure (OGSI) specifications (Foster et al., 2002). One design challenge is how to encode and store metadata in RDF. Currently, most RDF-based systems ask users to feed RDF metadata directly (Alexaki et al., 2001). Therefore, users have to encode resource metadata into RDF syntax manually, a process which is inconvenient and difficult. To cope with this problem, we have designed and implemented the DataAggregator, a component which generates the RDF syntax automatically and subsequently stores the RDF-encoded metadata in RDF storage. Another challenge is the storage and query of RDF metadata. Typically, an RDF database can be used to store RDF metadata, and an RDF query language can be used to express queries and execute them on the database. In the EU-MEDIN use-case scenario, however, most projects do not contribute large bodies of metadata; also, metadata updates are not very frequent. Therefore, a database system would be an overly expensive solution for our system requirements. Moreover, we would have to choose one among several existing RDF databases and query languages (Karvounarakis et al., 2002), and integrate it with gDisDL. Thus, the system would depend heavily on the chosen database system. Currently, the default behavior of gDisDL is to store RDF metadata in plain files. In order to make gDisDL more open and extensible, however, we have designed the Searcher component as a “query translator” that processes user requests and generates queries according to the back-end storage used in each gDisDL site. Thus, the back-end storage system remains transparent to the user and any RDF database system can be deployed with gDisDL. Another important design consideration is security. The security of the gDisDL is mainly concerned with two aspects: one is accessing distributed RDF metadata securely; another is that the shared RDF metadata should be protected from unauthorized access. In other words, the RDF metadata sharing of the gDisDL system should be governed by a set of rules and policies, such as what is shared, who is allowed to share, and the conditions under which sharing occurs. To address the security issues, we adopt the Secure Socket Layer (SSL) with the Grid Security Infrastructure (GSI) credentials in the gDisDL system (Freier, 1996). GSI credentials are based on X.509 certificates, and the GSI-enabled HTTP protocol (HTTPg) is used as the transport layer protocol to establish an encrypted connection between gDisDL clients and services (Housley et al., 2002).
gDisDL components The Data Aggregator. The Data Aggregator is a grid service that encodes information about EU-MEDIN resources into RDF. The RDF encoding work is done by creating a Jena RDF model in built-in memory and inserting the information from the Editor as a set of triples in Subject, Predicate, Object format using the Jena RDF framework (JENA, 2003). A Jena RDF model is an RDF graph in which RDF triples are represented as node-arc-node subgraphs. In a subgraph, the nodes are corresponding to the subject and the object of the triple; whereas the directed arc corresponds to the predicate. In particular, the Data Aggregator service:
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Gets the information describing an EU-MEDIN resource from the Editor. The Editor invokes the Data Aggregator service and sends it the resource information using SOAP messages via HTTPg POST command. Validates the provided information with respect to the EU-MEDIN RDF schema, taking into account resource classes, class properties, restrictions, and data types. If the information is deemed valid, the Data Aggregator will generate a unique URI to identify the resource. The URI contains two parts: one is the location of the gDisDL system that the user uses (e.g. the domain name); the other is the time when the RDF metadata was generated. If the data is not valid, the Data Aggregator returns a service fault message to the Editor, and ends the process. The Data Aggregator transforms the validated data together with the created URI into an RDF Jena model, which is a collection of triples, each consisting of a subject, a predicate and an object (Klyne and Carroll, 2002). The RDF metadata of the resource is thus created. The RDF metadata is encoded in RDF/XML format and saved in an RDF file.
By default, in a gDisDL node, RDF metadata for the same kind of EU-MEDIN resources (i.e. resources belonging to the same EU-MEDIN class) are stored into the same RDF file. For example, all RDF metadata describing journal papers are kept in one file, all RDF metadata describing data sets are kept in another file, and so on. Thus, when we look for metadata about journal papers, we can search into local or remote RDF files dedicated to journal-paper metadata. The Searcher. The Searcher is a grid service responsible for searching the distributed metadata and answering queries about the RDF resources. A Searcher grid service allows a client to query the RDF information held in the Jena models. The query of the Searcher service is RDF triple-oriented. In other words, an RDF triple pattern (e.g. {subject(?), predicate(?), object(?)}) will be generated according to the user’s request; and it is used to match the RDF triples in the Jena model. For example, a simple user request “Find reports from the CrossGrid project” specifies that the resource is about a Report whose predicate is projectacronyms and its object is “CrossGrid”. Therefore, the query triple pattern can be represented as {?x, disdl:projectacronyms, “CrossGrid”} of the Report RDF resource. The Searcher executes this query by matching all the RDF triples of the report resource in the RDF Jena model against the triple pattern, and retrieves a set of matches, which have project property (“p” ¼ disdl:projectacronyms), and value “CrossGrid” (“o” ¼ CrossGrid). As shown in Figure 4, the Searcher may need to search the RDF metadata not only stored locally (i.e. the RDF metadata is located in the gDisDL node that the Searcher service belongs to), but also stored remotely (the RDF metadata is stored in another gDisDL node) in order to answer a query. On purpose of simplicity, the Searcher will start with searching the RDF metadata that is stored “locally”. After receiving a request from a client, the Searcher checks the data of the request according to the RDF Schema, translates it into an RDF triple pattern. Next step, the Searcher will locate the RDF file that may contain the desired RDF metadata, and upload the RDF metadata into the Jena model as a set of RDF triples from the RDF file. After that, the Searcher
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Figure 4. Diagram describing the searching process
can explore all the RDF triples in the model (e.g. the name or URI of the resources and their properties), compare them according to the RDF triple pattern, and retrieve the matched RDF metadata. Finally, the client will get the matched RDF metadata from the Searcher in a SOAP message responding its query in an HTTPg GET option. In the case that the back-end storage is an RDF database system, the Searcher will “translate” the user’s request (i.e. the resource type, the property, values of the properties) into a proper RDF query language format, and then query the RDF database. If the RDF metadata is not stored locally, the Searcher will need to query the RDF metadata in a “remote” distributed gDisDL node. In this case, the Searcher service will be a client to invoke another Searcher grid service in order to search the desired RDF metadata that is stored there (see Figure 4). In other words, if the desired RDF metadata can not be found in the “local” gDisDL node, the Searcher will invoke another Searcher service. The process of remote search can be described as a series of steps (see also Figure 4): (1) The Searcher (S1) does not get the requested RDF metadata from the “local” node. The S1 grid service will act as a “client” to invoke another Searcher grid service in order to find the desired RDF metadata. (2) The “client” (i.e. the S1) first queries the UDDI server to find a properSearcher grid service based on the published GWSDL and SDE information. (3) S1 invokes another Searcher service (S2) and sends it the query using SOAP messages via HTTPg POST method. (4) S2 locates an RDF file that may contain the desired RDF metadata, and then uploads the RDF metadata as a set of RDF triples into the Jena model from the RDF file. (5) S2 searches the RDF metadata in this model. (6) The RDF metadata will be sent back to S1 in SOAP using HTTPg POST method. There are many Searcher services available in the distributed gDisDL nodes whilst the “client” (e.g. S1) needs to locate another Searcher service. In order to facilitate locating a “remote” Searcher service, the Searcher service provides information about the RDF metadata stored in the gDisDL node, such as the types of the gDisDL RDF resources,
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the different predicate index information of the RDF metadata stored in the gDisDL node (e.g., author index information, project index information). The information should be well structured and attached to a Searcher service. We adopt Service Data Elements (SDE) mechanism of the grid service for this purpose (Tuecke, 2002). The SDE of a grid service is a structured collection of information that is attached to the grid service. The information contained in the SDE will be published in the UDDI server and can be queried by the clients of the service. Currently the provided information is about the types of the gDisDL RDF resources. The structure and the content of the SDE that is attached to the Searcher grid service is shown below: 1 ,rdf:RDF 2 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntaxns#" 3 xmlns:dc="http://purl.org/dc/elements/1.1/" 4 xmlns:dlib="http://www.eu-medin.org/2003/02/Schema#" . 5 ,rdf:Description rdf:about="http://www.eumedin/gDisDL/gDStore1". 6 ,dlib:availability. plain file , /dlib:availability. 7 ,dlib:availability. RDF database ,/dlib:availability. 8 ,dc:type. plain RDF file , /dc:type. 9 ,dc:coverage. 10 ,rdf:Bag. 11 ,rdf:li. Project ,/rdf:li. 12 ,rdf:li. JournalPaper ,/rdf:li. 13 ,rdf:li. DataSet ,/rdf:li. 14 ,rdf:li. Software ,/rdf:li. 15 ,rdf:li. Hardware ,/rdf:li. 16 ,/rdf:Bag. 17 ,/dc:coverage. 18 ,dc:type. RDF database , /dc:type. 19 ,dc:language. RDQL , /dc:language. 20 ,/rdf:Description. 21 ,/rdf:RDF.
As you can see, lines 5 and 6 specify that the back-end storage is available both in plain RDF files and in the RDF database; lines 10 to 16 describe that in the RDF metadata there are about five kinds of RDF resources, namely Project, JournalPaper, DataSet, Software, and Hardware. Line 19 specifies that the query language of the RDF database is RDQL. The UDDI Server. The UDDI server of the gDisDL system is a provider-specific server that is used to publish the information about gDisDL grid services. It is also a registry of the gDisDL system where all the gDisDL grid service providers should register their gDisDL Grid service by submitting the description of the gDisDL grid services, access policy, security policy, and service data elements (SDE). The information is then published via the internet and a client in turn can use the published information to discover and invoke a desired gDisDL grid service. Two key pieces of data are maintained in an entry of the UDDI repository: the GWSDL file represented as a tModel, which represents the grid-service metadata in UDDI, and the binding information of the service implementation (UDDI version 3.0.2).
A client can find a desired gDisDL grid service by the tModel, and invoke the desired service by the binding information. gDisDL GUIs. Two graphical interfaces are designed to facilitate the end user: the Editor and the GUI Client of the Searcher. The Editor is a GUI client of the Data Aggregator grid service where a user can input information and data about EU-MEDIN resources. Similar to the EU-MEDIN portal, it also provides some forms which can be used to collect information about the EU-MEDIN resources. The user will manually input the information using the provided forms, and then the Editor will collect them in XML. The Editor invokes a Data Aggregator grid service, passing the collected information using SOAP message. By default, we set the “local” Data Aggregator service to the Editor. In OGSI, a client locates a grid service using its Grid Service Handle (GSH). The GSH of the “local” Data Aggregator service will thus be assigned to the Editor by default GSH (Tuecke, 2002). The Searcher GUI Client of the Searcher grid service is needed for users to input the parameters of metadata queries and get results (see Figure 5). The GUI allows users to specify the resource type, the property, values of the properties, etc. The GUI Client also decodes the RDF query results into human-readable form, and displays it on the result window (see Figure 5). The “local” Searcher service is also set as a default service for the Searcher GUI.
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Implementation In this section, we provide some details about our gDisDL prototype implementation. Figure 6 shows the layered architecture of the gDisDL grid services. The grid gDisDL is implemented within the Open Grid Services Infrastructure (OGSI). Globus Toolkit 3 and Jena are the main development tools used in our implementation. GT3 is a software toolkit that can be used to program grid-based applications. It is implemented in Java following the OGSI specification. GT3 provides several services, programs, utilities, etc. Jena is a Java API that can be used to create and manipulate RDF graphs. It is comprised of object classes to represent graphs, resources, properties, and literals; a graph is called a model and it is represented by the model interface. We use Jena RDF toolkit for creating, manipulating and querying RDF metadata. To implement a grid service, the first and most important task is to specify the interface of the grid service in Grid Web Service Description Language (GWSDL) (Sotomayor, 2003). Once the interface is correctly defined in GWSDL, implementing the service using Java and other tools is straightforward. Thus we will focus on describing how the interface is defined and what kind of operations can be invoked.
Figure 5. GUI of gDisDL searcher
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Figure 6. Layered view of the gDisDL grid services
Data Aggregator grid services and interface The Data Aggregator service processes the collected information and data from the Editor client, encodes it into RDF format, and saves it as RDF files. The interface of the DataAggregator grid service is defined in GWSDL, as shown below: ,gwsdl:portType name="DataAggregatorPortType" extends="ogsi:GridService". ,operation name="retriveInfo". ,input message="tns:GetInputMessage"/. ,output message="tns:GetOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation. ,operation name="getRDF". ,input message="tns:GetRDFInputMessage"/. ,output message="tns:GetRDFOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation. ,operation name="validate". ,input message="tns:ValInputMessage"/. ,output message="tns:ValOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation.
Operation/PortType. retrieveInfo() is used to get values of the RDF triples from a client; getRDF() creates an RDF graph and assigns the values of the triples; validate() checks and validates the input data according to the syntax of EU-MEDIN RDF metadata; saveRDF() saves the RDF metadata into a file in RDF/XML syntax.
Searcher grid services and interface The Searcher grid service is used for receiving and answering user queries about EU-MEDIN resources. When searching for RDF metadata, the Searcher can either use the RDF metadata document match() method to search the RDF metadata in an RDF file, or, alternatively, the search can be conducted upon an RDF database, using a database-specific plug-in. Currently we have implemented only the first case. The interface is defined as shown: ,gwsdl:portType name="SearcherPortType" extends="ogsi:GridService". ,operation name="preprocess". ,input message="tns:PreInputMessage"/. ,output message="tns:PreOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. , /operation. ,operation name="searchList". ,input message="tns:ListInputMessage"/. ,output message="tns:ListOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation. ,operation name="match". ,input message="tns:MatchInputMessage"/. ,output message="tns:MatchOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation . ,operation name="getStatements". ,input message="tns:GSInputMessage"/. ,output message="tns:GSOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/. ,/operation. ,operation name="insertStatements". ,input message="tns:ISInputMessage"/. ,output message="tns:ISOutputMessage"/. ,fault name="Fault" message="ogsi:FaultMessage"/.
Operation/PortType. The preprocess() operation is used for pre-processing user requests. The searchList() gets the remote RDF metadata information from the UDDI server. The match() operation is used to match RDF triples. The getRDFStatements() is used to fetch the desired RDF metadata. The insertStatements() operation allows for the insertion of RDF triples into the RDF Jena model.
Conclusions and future work In this paper, we presented an RDF-based grid service approach for organizing and capitalizing European research results in the field of natural disasters. Our approach allows the RDF metadata of European research results in the field of natural disasters to be shared securely and effectively in a heterogeneous network environment, using grid technology. We have described the design and the prototype implementation of gDisDL, an RDF-based, grid-enabled system. gDisDL system is a platform independent system which provides good interoperability with other systems. It can store, manage, and query RDF metadata in a secure and distributed manner.
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In the future, we plan to extend gDisDL with RDF-database plug-ins for supporting more efficient storage of RDF metadata, and to extend the searching mechanisms of the Searcher in order to integrate RDF databases into our system. Furthermore, we are investigating the development of a semantic UDDI that would improve the utilization of the gDisDL grid services. Our approach can be easily generalized to cope with metadata of different kinds and evolve as a generic search engine for RDF-encoded metadata posted on the grid. References Alexaki, S., Christophides, V., Karvounarakis, G., Plexousakis, P. and Tolle, K. (2001), “The ICS-FORTH RDFSuite: managing voluminous RDF description bases”, in Staab, S. (Ed.), Proceedings of the Second International Workshop on the Semantic Web (SemWeb ’01), pp. 1-13. Berners-Lee, T. (2000), Primer: Getting into RDF and Semantic Web using N3, The World Wide Web Consortium. Berners-Lee, T., Fielding, R. and Masinter, L. (1998), “Uniform Resource Identifiers (URI):Generic Syntax”, The Internet Engineering Task Force. Bray, T., Paoli, J., Sperberg-McQueen, C.M., Maler, E. and Yergeau, F. (2004), Extensible Markup Language (XML) 1.0, 3rd ed., World Wide Web Consortium. Brickley, D. and Guha, R.V. (2000), Resource Description Framework (RDF) Schema Specification 1.0, World Wide Web Consortium. Connolly, D., van Harmelen, F., Horrrocks, I., McGuiness, D.L. and Patel-Schneider, P.F. (2001), DAML þ OIL (March 2001) Reference Description, World Wide Web Consortium. Dublin Core (2003), “Dublin Core metadata element set, version 1.1: reference description”, available at: http://dublincore.org/documents/2003/06/02/dces EU-MEDIN (2003), EU-MEDIN portal, available at: www.eu-medin.org Foster, I., Kesselman, C., Tsudik, G. and Tuecke, S. (1998), “A security architecture for computational grids”, paper presented at the 5th ACM Conference on Computer and Communications Security Conference. Foster, I., Nick, J., Kesselman, C. and Tuecke, S. (2002), “The physiology of the grid: an open grid services architecture for distributed systems integration”, paper presented at the Open Grid Service Infrastructure WG, Global Grid Forum. Foster, I., Tuecke, S. and Kesselman, C. (2001), “The anatomy of the grid: enabling scalable virtual organizations”, Supercomputer Applications, Vol. 15 No. 3. Freier, A.O. and Paul, C. (1996), “The SSL Protocol Version 3.0”, Transport Layer Security Working Group. Housley, R., Polk, W., Ford, W., Solo, D. and “Internet, X. (2002) Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile”. JENA (2003), Jena – a Semantic Web Framework for Java, Hewlett-Packard, available at: http:// jena.sourceforge.net. Karvounarakis, G., Alexaki, S., Christophides, V., Plexousakis, D. and Scholl, M. (2002), “RQL: a declarative query language for RDF”, paper presented at the 11th International World Wide Web Conference (WWW’02), Honolulu, HI, May 7-11, Vol. 02. Klyne, G. and Carroll, J. (2002), Resource Description Framework (RDF): Concepts and Abstract Data Model, technical report, The World Wide Web Consortium.
Manola, F. and Miller, E. (2003), “RDF primer”. W3C Working Draft, available at: www.w3.org/ TR/rdf-primer/ Patel-Schneider, P.F., Hayes, P. and Horrock, I. (2004), OWL Web Ontology Language Semantics and Abstract Syntax, World Wide Web Consortium. Sotomayor, B. (2003), The Globus Toolkit 3 Programmer’s Tutorial, technical report, The Globus Alliance. Tuecke, S. et al. (2002), “Open Grid Service Infrastructure (OGSI) version 1.0”, Open Grid Service Infrastructure WG, Global Grid Forum.
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Ioannis Papadakis Department of Archives and Library Science, Ionian University, Corfu, Greece
Agapios Avramidis Department of Informatics, University of Piraeus, Piraeus, Greece, and
Vassilis Chrissikopoulos Department of Archives and Library Science, Ionian University, Corfu, Greece Abstract Purpose – Aims to bridge the gap between grid computing and semantic exploitation of information commonly met in digital library infrastructures. Design/methodology/approach – This paper introduces a semantic digital library framework based on grid technology. It follows the OGSA specifications for the development of grid infrastructures capable of efficiently handling such information. It is a service-oriented approach based on common web technologies such as the web browser and web server. The design principles of the proposed framework take into account the emerging need to exploit the semantics of its underlying information through the employment of adequate open standard technologies such as RDF and OWL. Findings – Although semantic exploitation of large data sets used to be a difficult and resource-consuming activity usually taking place in specialized, highly equipped laboratories, this work demonstrates that emerging technologies like the grid and emerging standards like RDF/OWL are capable of bringing such research closer to the average workstation. Research limitations/implications – The lack of a working prototype based on the proposed framework limits the usefulness of the results deriving from this paper. Originality/value – This paper can serve as a starting point to researchers wishing to conduct research in the area of the semantic grid as applied to digital library infrastructures. Keywords Digital libraries, Knowledge management, Data handling Paper type Research paper
Library Management Vol. 26 No. 4/5, 2005 pp. 246-260 q Emerald Group Publishing Limited 0143-5124 DOI 10.1108/01435120510596099
1. Introduction The current reality of the web resembles a place where humans and computers live together under the umbrella of a set of shared protocols and technologies. The inherently different nature of these two populations initially led to the development of various tools aiming at instructing the computers to accomplish certain tasks that humans had in mind. Examples of this trend are the http protocol and the HTML language. Although the initial goal was accomplished, as a side effect, such an effort resulted in the development of applications that failed to provide legible information about their content to humans. In order to overcome such hindrances, XML was introduced as a set of rules capable of generating languages with the ability to This paper has been produced under the auspices of the South Eastern European Grid-enabled eInfrastructure Development – SEE-GRID project (SeeGrid, 2005).
encapsulate content-specific information that was available both to computers and humans. The next step to the evolution of the web is the creation of systems capable of making computers and reason with the semantics that humans have in mind about the underlying information. In this direction, various standards have emerged with distinctive examples the Resource Description Framework – RDF (W3C, 2004) and the Web Ontology Language – OWL (W3C, 1999b). From another point of view, the web is currently a place where people want to share information. In order to facilitate this trend, a number of systems have emerged that realized the notion of distributed computing. One of the most important classes of systems realizing the notion of distributed computing is digital libraries. Digital libraries emerged as a way to fulfill the requirement of efficiently exploiting large and diverse information resources by providing them with structure and organization as well as applying a number of services to these resources. The first distributed, web-based digital libraries implemented the client-server architecture through the employment of common technologies like the web server-web browser. The next step was to isolate business logic from the original client-server model into a third layer, thus realizing systems that followed the three-tier model. Although three-tier (an extended version of three-tier systems are the n-tier systems) systems proved to be a significant improvement in the area of distributed computing, it was not long before peer to peer systems emerged as a way to get advantage of the effectiveness of the increasingly high number of online workstations that managed to provide high performance in an affordable price. Such systems (Gnutella, Napster etc) became very popular among the users of the web. Nowadays, the need to share information featuring high degree of size and diversity in a consistent, transparent and at the same time efficient way, urged researchers to shift their efforts towards the realization of grid infrastructures. Such infrastructures hide the distribution details of software and hardware and provide users with ready to use services that exist transparently within the web. In this paper, a semantic digital library framework based on grid technology is introduced, which is an effort to apply and at the same time extend Open Grid Services Architecture – OGSA’s guidelines (Foster et al., 2002) for grid computing into robust, extensible, open-source working digital libraries. The proposed framework focuses on the efficient management of diverse data resources that are logically organized into documents. The special nature of such information (large amount of data possibly originating from many contextually different areas) motivated the authors of this paper to give special attention to the underlying semantics and specifically to find ways of semantically exploiting such information through the employment of equivalent standards such as RDF and OWL. The rest of this paper is organized as follows: in the next section, related work in the field of distributed systems is presented. Section 3 describes the proposed framework, focusing on the semantics of the underlying information as well as the core services that are provided. Section 4 addresses several issues concerning the semantic aspect of the proposed framework. Finally, section 5 concludes this paper and points out directions for future work. 2. Related work Recent trends in software engineering dictate that service-oriented approaches should be employed in order to simplify the design, development and maintenance of deriving
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systems. Following these trends, the field of distributed computing introduced the web services paradigm, which also allows web-oriented systems to take advantage of distributed computing capabilities. In this context, Fedora (Payette and Lagoze, 1998) was introduced as a digital library system, which provides the underlying functionality through web services. A fundamental concept in Fedora is the Digital Object, which is defined as a content container abstraction. The Digital Object consists of digital resources sharing the same context and their corresponding metadata. Such metadata include system metadata that facilitate the management of the digital object and descriptive metadata that provide information about the corresponding digital resources. Users obtain the content of a Digital Object through the use of disseminators, which transform digital data into recognizable informational entities such as books, multimedia encyclopedias, etc. Services aiming at the semantic exploitation of digital objects are not directly supported by Fedora. Such functionality could be provided by user-defined disseminators. However, the simplistic nature of the protocols defining web services posed certain inefficiencies concerning but not limited to lifecycle management and state information, as stated in (Foster and Kesselman, 1997). To overcome these inefficiencies, digital library systems may adopt grid services (Foster et al., 2002) as an implementation vehicle. In this direction, the GridIR (GridIR, 2005) initiative introduced protocols for the information retrieval process, based on grid services. In particular, a GridIR implementation should be segmented into three fundamental IR tasks or functional groups, namely: collecting documents into a local store, indexing or other preparation of documents, and querying one or more document collections and associated retrieval of search results. Currently, GridIR lacks on semantic analysis of the underlying information since no formal method for semantic analysis has been proposed. As far as semantic analysis in distributed systems is concerned the Interspace project proposes a prototype for supporting semantic indexing and retrieval analysis. The general goal of the Interspace prototype is to unify disparate distributed information resources in one coherent model. However, the lack of employment of standards renders this approach difficult to be adopted by adequate systems. 3. A semantic-capable grid framework 3.1 Purpose Recent activity on the web and in distributed computing in general, specifies that data should have a semantic meaning not only for humans that read such data but also for computers that manipulate it (Berners-Lee et al., 2001). Until recently, research towards this direction relied on extremely computational intensive statistical methods that required high-end supercomputers and could only be applied to relatively moderate document collections. The rapid growth of computer networks and distributed-parallel computing, inspired many researchers to shift their efforts of semantic manipulation of large and diverse document collections from dedicated, well-equipped labs to the average workstation (Bennett et al., 1999). However, the lack of adequate standards promoted the development of highly specialized, monolithic systems that seldom contributed to reusability. The main goal of this paper is to propose a methodology of applying the capabilities of grid computing in resource management to the field of semantic analysis of large
document collections. Such an approach is based on standards not only from the grid environment (i.e. OGSA) but also from the Semantic Web (OWL, RDF) in an effort to decrease complexity and preserve openness, reusability and interoperability for the resulting outcomes. In this direction, a semantic-capable grid framework for digital libraries is introduced as an infrastructure capable of applying semantic analysis to large document collections. The proposed framework follows the OGSA specifications and thus can be applied to a distributed environment consisting of workstations of varying computational capabilities. The employment of standards within this approach dictates that such an infrastructure could be applied to a number of environments featuring similar requirements. 3.2 General description In this section, a semantic capable digital library framework is presented, which is based on the OGSA guidelines. The proposed framework is accessible through the web, since the provided GUI is compliant with common web server-web browser technologies. Such approach simplifies the maintenance of the overall system and provides for the wider dissemination of the resulting systems. The cell of the proposed framework’s data repository is the “document”, which is defined as “a set of various resources under the same context”. A document may therefore be comprised of various resources varying from simple text files to complex aggregations of files with diverse formats. Each document is accompanied by metadata represented as an RDF-based structure containing various descriptive information about each document. Metadata contain elements like “department” or “thematic area” that aid in the development of different views of the entire information space. The employment of the RDF standard for the encoding of such information within the underlying repository, renders the proposed approach capable of delivering semantic-flavored grid services to its users. It is the authors’ belief that supporting such functionality is mandatory for systems aiming at participating in the new era of the semantic web. The proposed framework is service-oriented in the sense that the provided functionality is expressed through a set of low-level as well as high-level grid services similar to (Newhouse et al., 2002), complying to the OGSA specifications. An overall description of the framework is illustrated in Figure 1. Apart from these core grid services, a number of supplemental client-side services are also provided, that contribute to the QoS of the resulting digital libraries. Such services are executed mainly at the Web browser located at the client-side, through client-side technologies such as JavaScript and XSLT (W3C, 1999a). According to (Papadakis et al., 2002), the benefits from employing such a methodology is that client-side services rely on client computer’s resources without putting additional strain to the grid or reserving unnecessary network bandwidth. For example, users are able to manipulate long lists of search results locally at their browsers through ranking and/or filtering services without exchanging unnecessary data with the grid. 3.3 Document’s metadata Quite often, many multi-disciplined environments (e.g. universities, healthcare, etc) need to be able to clearly and promptly distinguish resources that share the same
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Figure 1. Framework
semantics. Furthermore, such semantics should be easily expressed, in order to facilitate efficient knowledge acquisition, modeling and reuse (Tao et al., 2003). In this context, users wishing to submit a document into systems deriving from the proposed framework, are prompted to provide descriptive information about their work. Such information is encoded under the qualified Dublin Core guidelines (Dublin Core, 2002) and constitutes each document’s metadata. Apart from core metadata elements that are required for each document (i.e. DC.Creator, DC.Language, etc) the proposed framework promotes richer semantic description of its underlying resources by proposing a form-based authoring tool capable of utilizing the concepts and relations between these concepts within a hierarchy of ontologies in OWL format, describing each different concept space. For example, within the University environment, a hierarchy of ontologies could be defined describing each different scientific area. Thus, through the employment of ontologies provided by the Lehigh University in (Guo et al., 2004), users are able to further describe the context of their submitted work, e.g. by providing information about their position within the University (e.g. associate Professor, assistant Professor, etc) and the relevance of their work as compared to similar submissions within the digital library. Such descriptions could be used to address semantic-related queries, such as “what other professionals with the same position within the University have published similar papers? ”. In order to keep such information, apart from the employment of qualified Dublin Core elements, the proposed framework suggests the mapping of such elements in adequate RDF structures, thus providing information that corresponds to the metadata of each document. This could possibly be achieved by extending the DC-RDF metadata encoding proposed in (Nelson, 2001). Furthermore, each document is assigned a DOI identifier (International DOI Foundation, 2005), which uniquely identifies this composite resource not only to the digital library, but also to the entire web. The adoption of the DOI handling system renders the proposed framework highly interoperable with similar approaches in the field of web-based digital libraries. Although the initial goal of this work is to describe a conceptual framework for the creation of semantic-capable digital library systems based on grid technology, without
dealing with various implementation issues that derive from such research, it is the authors’ belief that the most suitable method for implementing an authoring tool capable of providing semantic-flavored metadata to the submitted documents, should be based on forms technology. The forms interface guides users through the underlying ontology using dynamic menus and enable them to browse and search for existing concepts to assist their reuse (Buckingham Shum et al., 2000). Moreover, as compared to alternative approaches such as annotation tools (Mulholland et al., 2001) and concept maps (Buckingham Shum et al., 2000), users are more accustomed to forms due to the fact that they resemble the most widely spread web paradigm (see www. lehigh.edu/ zhp2/2004/0401/univ-bench.owl). 3.4 Provided services This section provides an overview of the basic services that are part of the underlying framework. These services extend the functionality of core grid services (Tuecke et al., 2003), thus facilitating discovery, access and manipulation of digital library data resources, organized as documents. Since the proposed services are essentially extended versions of common grid services, they also operate under the context of a Grid Container (Globus Toolkit (Foster and Kesselman, 1997)) and take advantage of its service-oriented architecture. Moreover, as it will be discussed in the following sections, the described services are capable of exploiting the functionality of existing, low level grid services in order to implement common tasks, such as data management (GridFTP (Allcock et al., 2002), RFT (Allcock et al., 2004)), security (CAS (Welch et al., 2003)), resource management (GRAM) and information discovery (MDS (Czajkowski et al., 2001)). Figure 2 provides an overview of the interaction between the proposed services and their corresponding low level grid services. According to the proposed framework, three core services are provided: (1) the document publication service, which accepts documents into the digital library; (2) the information retrieval service, which is responsible for the discovery of documents within the digital library; and (3) the document delivery service, which delivers the resources constituting a document to the client. All services are obtained by the client through the Factory operation as specified in (Foster et al., 2002). Specifically, upon initialization from a client, a ”create” operation is invoked at the Factory, which results in the creation of the corresponding service instance. At the same time, a locator for the newly created instance is delivered to the client. Finally, the service instance is registered to the framework by adding a record to the Registry, as it will be described in the following section. The aforementioned services operate in a secure context, which is provided by the employment of the Grid Security Infrastructure – GSI (Welch et al., 2003). GSI defines a common credential format based on X.509 (Welch et al., 2004) identity certificates and a common protocol based on the transport layer security (TLS) (Diecks and Allen, 1999). An X.509 certificate, in conjunction with an associated private key, forms a unique credential set, which is employed by every service to authenticate itself to other services.
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Figure 2. The service-based architecture
3.4.1 The Registry. At the heart of an OGSA-compliant framework such as the one proposed in this paper, the registry component is most commonly met. The registry consists of two major parts. An information structure, which stores metadata about the underlying services and a notification mechanism as defined in Tuecke et al. (2003), which is responsible for the subscription of services to specific events. According to the proposed approach, the Information structure is defined as follows; in order to provide semantic functionality to services, the framework defines two types of metadata that accompany each document: (1) application-specific metadata; and (2) context-specific metadata. On the one hand, application-specific metadata provide: . information about data access methods and the underlying transport protocols; and . information about the location of distributed resources constituting a document. Such information is encoded under the URI standard.
On the other hand, the structural and semantic aspects of documents are captured by context-specific metadata, which promote the employment of adequate semantic services, as it will be described in the following sections. Both types of metadata are integrated into a hash table where the key of each record is a DOI identifier, which is obtained by the document publication service. Moreover, both metadata sets are encoded under the RDF standard. The notification mechanism allows services to express their interest for a particular event by establishing a subscription for that event. An event may be generated by another service performing insert/update/delete operations upon the information structure. The notification mechanism provides an XML subscription language flexible enough to define when to generate an event (e.g. when a specific attribute of the metadata part of a record is modified) and bind this event to a specific action (e.g. update all services that provide indices based on the modified metadata). Figure 3 describes the functionality of the Registry. 3.4.2 Document publication service. The document publication service (Figure 4) provides operations utilized by users wishing to submit documents to digital libraries based on the proposed framework. Particularly, users may: . insert/update/delete data resources that constitute a document; and . insert/update/delete metadata that describe a document.
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Figure 3. The Registry
Figure 4. Document publication service
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As described earlier in this paper, users acting as authors are able to properly describe their work by providing semantic-flavored metadata through an adequate authoring tool. Such metadata is ultimately encoded in RDF format. According to the document publication service, the resulting RDF structure along with the resources that constitute a document are both submitted to the underlying digital library. The grid container is responsible for managing the interaction with users wishing to become authors within the system. Specifically: (1) In step 1 a user requests the document publication service. Assuming the user has already been authenticated to the digital library, the grid container examines his/hers credentials. If access is granted, a publication service instance is returned to the user, identified by a Grid Service Handle (GSH). The process of passing the document’s data resources and metadata is also performed in this step. (2) In step 2 the document publication service validates the user’s input (i.e. metadata þ data resources) and contacts the Grid Information Service (GIS), to check if the necessary storage is available. (3) In step 3 the document publication service invokes services responsible for accessing the system’s secondary storage, which are responsible for locating the storage space where the data will be kept. Currently, these services rely on the GridFTP (Allcock et al., 2002) to transfer data resources. The granularity of the overall grid infrastructure allows the adoption of alternative approaches that are expected to emerge in terms of access to the secondary storage of the system. (4) According to step 4, the document publication service is responsible for contacting an appropriate Registration Agency (RA) (International DOI Foundation, 2005) in order to obtain a DOI prefix and accordingly assign a unique DOI identifier to the submitted document. Part of the document’s metadata are published to the RA as well, which in turn deposits the doi and its corresponding URI to the DOI system. It is the authors belief that existing and emerging RAs like CrossRef (CrossRef, 2000), will be capable of extending the deriving digital library’s functionality, thus providing high QoS to its users. (5) Finally, in step 5 the document publication service contacts the Registry and adds a new record. The key of this record is the document’s doi identifier. Its corresponding value includes the context-specific metadata as well as various application-specific metadata as stated earlier in this paper. 3.4.3 Information retrieval service. In order to locate relevant documents on behalf of a user, the information retrieval service – IR service – is invoked. This service consists of various components. Specifically, the query processor service receives the search criteria from the IR GUI through the formquery operation and transforms them to a query that is addressed to every node in the grid that contains indices. It should be mentioned, that according to the proposed framework a number of possibly redundant indices reside in dedicated nodes of the grid infrastructure. The IndexLocator service is responsible for locating nodes containing the necessary indices. This service queries a hierarchical structure of Grid Information Index Servers-GIIS similar to (Czajkowski et al., 2001), to find the appropriate Grid
Information Servers (GIS). The GISs provide information about the location (e.g. index A resides in node B) and the content (e.g. index A is about Authors) of indices. The nodes that will eventually participate in the IR process maintain distributed indices constructed by the ManageIndex service. The ManageIndex service gets the context-specific metadata of each document and accordingly updates a set of distributed indices. Such a procedure is event-driven, in the sense that indices are updated upon arrival and/or modification of a document through the notification mechanism as explained in the registry section. Furthermore, the QueryIndex service provides an interface capable of querying its underlying indices. The matched records of each index are received by the QueryProcessor service which contains a merge operation, capable of merging the records from various digital library nodes into a single XML structure thus forming a search results list. This list is serialized to the client-side through the serialize operation, which employs XSLT technology. The user is able to get the resources within a document through the employment of the document delivery service, described later in this paper. Further processing of search results is possible through the functionality provided by client-side services. Figure 5 illustrates the interaction between the various sub components of the IR service. However, the effectiveness of searching in an information space that only uses precise term matching is rather low. Most frequently, a given query fails to accurately express the true information needs of a user. Since the results depend on the specific terms that constitute the original query, the quality of the result is therefore dependent of the query’s quality. To make things worse, it is often the case that the size of the result set is too large to handle. This is due to the fact that the result set contains various semantic meanings that a term might have although most of these meanings may not be relevant in the current context (Bennett et al., 1999). In order to deal with such implications, computational intensive statistical analysis can be applied to the underlying document collections. Thus, such analysis promotes term suggestion of alternative terms semantically related to the original query terms. Moreover, as illustrated in the Interspace project (Bennett et al., 1999), semantic indexing may also be provided. According to this method, a set of concepts are produced for each document by the author. These concepts representing concepts in
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Figure 5. Information retrieval service
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Figure 6. Document delivery service
the document, are suggested to the author by the system, which extracts them from the ontologies. Then, such concepts are categorized into a hierarchical set of category maps that facilitate navigation through adequate visualization and interpretation techniques. The employment of ontologies together with the availability of semantic-related metadata, eliminate the need for statistical parsing of documents within digital libraries deriving from the proposed framework. Moreover, the inherent flexibility of a distributed grid infrastructure to handle computational intensive semantic analysis and reasoning efficiently and in a scalable fashion as well, render this work as a suitable framework for the development of semantic-aware digital libraries. Apart from the aforementioned grid services, the proposed framework provides supplementary client-side services aiming at the even more efficient management of computer resources. Specifically, the IR service is further extended by the interactive search results service, which is executed at the client-side. According to this service, users are able to interact with the search results list in terms of ranking and/or filtering the included entries. This is possible by taking advantage of the context-specific metadata that accompany each entry. For example, a list of references to computer science documents in a University digital library, could be further elaborated by ranking the documents according to their size, or by filtering the list to include documents of just a specific author. Such functionality is based on the fact that the search results list is expressed in XML format. Specifically, the XML structure representing the search results is manipulated by the user through software modules executed at the client-side written in an adequate language such as XSLT and Javascript. Further details of the notion of interactive search results are provided in (Papadakis et al., 2002). 3.4.4 Document delivery service. The IR service described above provides the user with one or more relevant documents. It is the document delivery service’s responsibility to deliver the selected document to the client’s desktop. The scenario being followed (illustrated in Figure 6) in this case is described below: (1) Initially, the agent acting on user’s behalf calls the document delivery service, for a document identified by its corresponding doi identifier, which is provided from the previously described IR service. (2) The document delivery service passes the DOI to the Registry. (3) The Registry returns to the document delivery service the document’s metadata.
(4) The document delivery service invokes the GridFTP service. (5) The GridFTP service returns the document. (6) The document delivery service delivers the document to the user. 4 Semantics The proposed framework is a web-enabled grid digital library infrastructure capable of handling large amounts of diverse data resources commonly met in various domains and perform a number of computationally intensive actions upon such data resources. However, the evolution of the web dictates that further research should be conducted in the direction of the semantic grid (De Roure et al., 2004). As intuitively stated in (Decker et al., 2000), the web is entering in its third generation, passing through the first generation static web consisting of handwritten HTML pages followed by the second generation web consisting of machine generated and often active HTML pages. Nowadays, the web is evolving to the Semantic Web, aiming to the understanding and sharing of knowledge between humans and computers in a bi-directional way. In this context, the proposed framework should be designed in a way that promotes semantic exploitation of the underlying information. In order to achieve such a goal, it should extend common Grid functionality in a way that information and services are given well-defined meaning, thus better enabling computers and people to work in cooperation (Newhouse et al., 2002). In this context, the notion of the ontology is employed, which is defined as an “explicit specification of a conceptualization” (Gruber 1993). A more comprehensive definition argues that an ontology defines a “common vocabulary of terms, some specification of the meaning of the terms and a shared understanding for people and machines” (Goble and De Roure, 2002). Unfortunately, so far, there are neither well-established standard methodologies for building ontologies nor a field of ontological engineering equivalent to knowledge or software engineering. Despite these difficulties, the practical need to promote Tim Berner-Lee’s vision of the Semantic Web has promoted the development of significant initiatives towards this direction, like RDF, RDF(S) and OWL-S, which is an ancestor of DAML-S þ OIL, as well as ontology oriented tools like OilEd (Bechhofer et al., 2001) and Protege 2000 (Noy et al., 2001). Based on the above resources, a number of projects have evolved (Bachler et al., 2004), (Oberle et al., 2004) that aim to the semantic exploitation of information. In order to remain compatible with the emerging semantic Web, the proposed framework provides RDF-encoded metadata and follows the progress made to important Semantic Web initiatives like OWL-S (W3C, 1999b). Based on these guidelines, digital libraries based on this framework are expected to extend service discovery through the employment of expert-designed ontologies, following the steps of (Stevens et al., 2003). Furthermore, the need to provide extensive markup and annotation functionality will be facilitated through the development of web browser plug-ins taking advantage of the RDF/OWL representation of metadata as it has been described earlier in this paper. Moreover, intelligent interfaces taking advantage of the above functionality will promote the delivery of semantically rich documents by their authors, which in turn will facilitate the provision of semantic information retrieval (i.e. deliver integrated answers extended by derived facts (Goble and De Roure, 2002)). Finally, a number of
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semantic grid services are under discussion among the framework’s team members, ultimately aiming at the development of inference services operating over common data capable of delivering the right content, to the right person/system, in the right form at the right time (Goble and De Roure, 2002).
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5 Conclusions – future work In this paper a framework for the creation of semantic-capable grid digital libraries has been presented. It follows the OGSA guidelines for grid infrastructures and complies with current advances of the Globus Team and the Global Grid Forum-GGF (Foster et al., 2002). Moreover, special attention is given to the semantic aspect of information that lies within. Following Tim Berners-Lee’s vision for the Semantic Web, the proposed framework tries to bridge the gap between grid infrastructures and semantic analysis. The grid has tremendous potentials in distributed computing as far resource management is concerned, whereas semantics require large amounts of computer resources in order to scale efficiently in large data sets. In this context, the main motivation of this work was to integrate current trends in grid computing with semantic analysis of information organized as documents within a digital library environment. A fundamental principle that has been followed throughout this paper, suggests the employment of standards like RDF, OWL and the commitment to widely accepted initiatives like the OGSA guidelines for grid infrastructures. This way, emerging systems like the ones that will be based on the proposed framework will remain scalable and interoperable, thus providing higher quality of services to their users and avoid turning out to isolated, monolithic systems incapable of following future trends of the digital library society. As far as the semantic aspect of the proposed framework is concerned, future work is focused on the development of a consistent and semantically correct hierarchy of ontologies, which will in turn contribute to the employment of a number of semantic services that will greatly enhance the provided functionality. Moreover, the authors are working on the specifications of the extended qualified DC-RDF based language that will enable the expression of metadata of each document within the digital library in a way that will allow reasoning of such metadata with the underlying OWL hierarchies. Finally, a number of visualization techniques are being examined in order to properly present semantic relations between documents in search results lists and navigation templates. References Allcock, B., Bester, J., Bresnahan, J., Chervenak, A., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D. and Tuecke, S. (2002), “Data management and transfer in high performance computational grid environments”, Parallel Computing Journal, Vol. 28 No. 5, pp. 749-71. Allcock, W., Foster, I. and Madurri, R. (2004), “Reliable data transport: a critical service for the grid”, paper presented at the Building Service-based Grids Workshop, Global Grid Forum 11. Bachler, M., Shum, S., Chen-Burger, J., Dalton, J., Eisenstadt, M., Komzak, J., Michaelides, D., Page, K. and Potter, S. (2004), “Collaborative tools in the Semantic Grid“, paper presented at the GGF 11 Semantic Grid Applications Workshop, Honolulu, HI.
Bechhofer, S., Horrocks, I., Goble, C.A. and Stevens, R. (2001), “Oiled: a reason-able ontology editor for the Semantic Web”, KI ’01: Proceedings of the Joint German/Austrian Conference on AI, Springer-Verlag, Berlin. Bennett, N.A., He, Q., Chang, C.T.K. and Schatz, B.R. (1999), Concept Extraction in the Interspace Prototype, technical report, CANNIS. Berners-Lee, T., Hendler, J. and Lassila, O. (2001), “The Semantic Web”, Scientific American, Vol. 284 No. 5, pp. 34-43. Buckingham Shum, S., Motta, E. and Domingue, J. (2000), “ScholOnto: an ontology-based digital library server for research documents and discourse”, International Journal on Digital Libraries, Vol. 3 No. 3, pp. 237-48. CrossRef (2000), The CrossRef Working Group, Lynnfield, MA, available at: www.crossref.org Czajkowski, K., Fitzgerald, S., Foster, I. and Kesselman, C. (2001), “Grid information services for distributed resource sharing”, Proceedings of the 10th IEEE International Symposium on High-Performance Distributed Computing (HPDC-10), IEEE Press, San Francisco, CA. Dublin Core (2002), The Dublin Core Metadata Initiative, available at: http://dublincore.org/ documents/dcq-rdf-xml/, Dublin Core. De Roure, D.C., Gil, Y. and Hendler, J.A. (2004), IEEE Intelligent Systems, Vol. 19 No. 1, special issue on e-science. Decker, S., Harmelen, F., Broekstra, J., Erdmann, M., Fensel, D., Horrocks, I., Klein, M. and Melnik, S. (2000), “The Semantic Web – on the respective roles of XML and RDF”, IEEE Internet Computing, Vol. 4 No. 5. Diecks, T. and Allen, C. (1999), The TLS Protocol, Version 1.0, IETF, available at: http://www. ietf.org/rfc/rfc2246.txt Foster, I. and Kesselman, C. (1997), “Globus: a metacomputing infrastructure toolkit”, International Journal of Supercomputer Applications, Vol. 11 No. 2, pp. 115-28. Foster, I., Kesselman, C., Nick, J. and Tuecke, S. (2002), The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration, technical report, OGSI-WG, Global Grid Forum, available at: www.globus.org/research/papers/ogsa.pdf Goble, C. and De Roure, D. (2002), The Grid: An Application of the Semantic Web, Technical Report 4. GridIR (2005), The Grid Information Retrieval Working Group, available at: http://www.gridir. org/ Gruber, T. (1993), “A translation approach to portable ontologies”, Knowledge Acquisition, Vol. 5 No. 2, pp. 119-220. Guo, Y., Pan, Z. and Heflin, J. (2004), “Choosing the best knowledge based system for large Semantic Web applications”, WWW Alt. ’04: Proceedings of the 13th International World Wide Web Conference on Alternative Track Papers and Posters, ACM Press, New York, NY, pp. 302-3. International DOI Foundation (2005), The Digital Object Identifier System, International DOI Foundation, Oxford, available at: www.doi.org Mulholland, P., Zdrahal, Z., Domingue, J., Hatala, M. and Bernardi, A. (2001), “A methodological approach to supporting organizational learning”, International Journal of Human-Computer Studies, Vol. 55 No. 3, pp. 337-67. Nelson, M. (2001), Metadata: Use of Dublin Core Qualifiers, technical report, Physiome Sciences, available at: www.cellml.org/private/progress\s\do5(r)eport/20010326\s\do5(m)eeting\s\ do5(m)inutes.pdf
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Newhouse, S., Mayer, S., Furmento, S., McGough, S., Stanton, J. and Darlington, J. (2002), “Laying the foundations for the semantic grid”, paper presented at the: AISB Symposium on AI and Grid Computing, Imperial College, London. Noy, N.F., Sintek, M., Decker, S., Crubezy, M., Fergerson, R.W. and Musen, M.A. (2001), “Creating Semantic Web contents with protege-2000”, IEEE Intelligent Systems, Vol. 16 No. 2, pp. 60-71. Oberle, D., Volz, R., Motik, B. and Staab, S. (Eds) (2004), An Extensible Open Software Environment, chapter III, Springer, Berlin, pp. 311-3. Papadakis, I., Andreou, I. and Chrissikopoulos, V. (2002), “Interactive search results”, Proceedings of the 6th European Conference on Digital Libraries-ECDL 2002, Elsevier, Rome, pp. 448-62. Payette, S. and Lagoze, C. (1998), Flexible and Extensible Digital Object Repository Architecture (Fedora): Proceedings of European Conference on Digital Libraries-ECDL 98, Elsevier, pp. 41-59. SeeGrid (2005), The South Eastern European Grid-enabled e-Infrastructure – SEE-GRID project, available at: www.see-grid.org Stevens, R., Robinson, A., Goble, C. and Curt, A. (2003), “mygrid: personalized bioinformatics on the information grid”, Proceedings of 11th International Conference on Intelligent Systems in Molecular Biology, Vol. 19, pp. 302-4. Tao, F., Cox, S., Chen, L., Shadbolt, N., Xu, F., Puleston, C., Goble, C. and Song, W. (2003), Towards the Semantic Grid: Enriching Content for Management and Reuse: Proceedings of Delivering e-Science, UK e-Science All-hand Conference 2003, pp. 695-702. Tuecke, S., Czajkowski, K., Foster, I., Frey, J., Graham, S., Kesselman, C., Maquire, T., Sandholm, T. and Snelling, D. (2003), Open Grid Services Infrastructure (OGSI) Version 1.0, Global Grid Forum, technical report, available at: www.globus.org W3C (1999a), Extensible Stylesheet Language Transformations (XSLT) specifications, World Wide Web Consortium, available at: www.w3.org/Style/XSL/ W3C (1999b), Ontology Language (OWL) Model and Syntax specifications, World Wide Web Consortium, available at: www.w3.org/OWL. W3C (2004), Resource Description Framework Syntax Specification – RDF Syntax, World Wide Web Consortium, available at: www.w3.org/RDF/ Welch, V., Foster, I., Kesselman, C., Mulmo, O., Pearlman, L., Tuecke, S., Gawor, J., Meder, S. and Siebenlist, F.X. (2004), “X.509 Proxy certificates for dynamic delegation”, Proceedings of the 3rd Annual PKI R&D Workshop. Welch, V., Siebenlist, F., Foster, I., Bresnahan, J., Czajkowski, K., Gawor, J., Kesselman, C., Meder, S. and Pearlman, L. (2003), “Security for grid services”, 12th International Symposium on High Performance Distributed Computing (HPDC-12), IEEE Press, Los Alamitos, CA.
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Mining novel connections from large online digital library using biomedical ontologies Xiaohua Hu
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College of Information Science and Technology, Drexel University, Philadelphia, Pennsylvania, USA Abstract Purpose – The huge volume of biomedical literature provides a nice opportunity and challenge to induce novel knowledge by finding some connections among logical-related medical concepts This paper aims to propose a semantic-based knowledge discovery system for mining novel connections from large online digital libraries. Design/methodology/approach – The method takes advantages of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system, Biomedical Semantic-based Knowledge Discovery System (Bio-SbKDS), is designed to uncover novel hypothesis/connections hidden in the biomedical literature. Using only the starting concept and the initial semantic relation derived from UMLS, Bio-SbKDS can automatically generate the semantic types as category restrictions for concepts. Using the semantic types and semantic relations of the biomedical concepts, Bio-SbKDS can identify the relevant concepts collected from Medline in terms of the semantic type and generate the novel hypothesis between these concepts based on the semantic relations. Findings – The system successfully replicates Dr Swanson’s famous discoveries: Raynaud disease/fish oil automatically, and generates much less intermediate concepts and spurious connections. Originality/value – The method takes full advantage of the semantic knowledge of the biomedical concepts, compared with previous approaches, our methods generate much less but more relevant novel hypotheses. Another significant advantage over other traditional approaches is that our method requires much less human intervention in the discovery procedure. Keywords Digital libraries, Knowledge management, Medical informatics, Data handling Paper type General review
Introduction The problem of mining novel connection (also known as undiscovered public knowledge) from biomedical literature was exemplified by Swanson’s pioneering work on Raynaud disease/fish-oil discovery in 1986. Back then, the Raynaud disease had no known cause or cure, and the goal of his literature-based discovery was to uncover novel suggestions for how Raynaud disease might be caused, and how it might be treated. During Swanson’s initial readings in around 560 documents that discussed Raynaud disease in the most recent five-year period in 1985, he found that those literature mention that Raynaud disease is a peripheral circulatory disorder aggravated by high platelet aggregation, high blood viscosity and vasoconstriction. This work was supported in part by the research grants from the PA Department of Health (No. 240205, 240196).
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In another separate set of literature on fish oils, he found out those articles show the ingestion of fish oil can reduce these phenomena. But no single article from both sets in the biomedical literature mentions Raynaud and fish oil together in 1985. Putting these two separate literatures together, Swanson hypothesized that fish oil may be beneficial to people suffering from Raynaud disease. (Swanson, 1986a, b) This novel hypothesis was later clinically confirmed by DiGiacome et al. (1989). Swanson’s methods require very strong domain knowledge especially in the process of qualifying the intermediate concepts (Swanson calls it the “B” concepts or “bridge” concept in his method) and a lot of human intervention in the discovery procedure. Several approaches (Srinivasan, 2004; Joshi et al., 2004; Pratt and Yetisgen-Yildiz, 2003; Hristovski et al., 2003; Weeber et al., 2003; Lindsay and Gordon, 1999) have been developed to automate this discovery process based on Swanson’s method and have successfully replicated the Raynaud disease/fish-oil, migraine/magnesium discovery, discover new treatment for other diseases such as thalidomide. These research works have produced valuable insights into new hypothesis, however substantial manual intervention has been required to reduce the number of spurious connections. In this paper, we present a semantic-based approach for mining novel connection from biomedical literature. Our approach replaces manual ad-hoc pruning by using semantic knowledge from biomedical ontologies. We use the semantic information to manage and filter the sizable branching factor in the potential connections among the huge number of medical concepts. We have developed an interactive knowledge discovery system, Bio-SbKDS (Biomedical Semantic-based Knowledge Discovery System) that can uncover the hidden connections or associations among concepts (MeSH terms, here) from the complementary non-interactive biomedical literature. In order to solve the ambiguity problem of the biomedical terms, and discover the novel hypothesis from the huge search space of possible connections among the biomedical concepts in an effective and efficient way, we utilize the biomedical ontologies, such as UMLS and MeSH. Unlike other approaches (Srinivasan, 2004; Joshi et al., 2004; Pratt and Yetisgen-Yildiz, 2003; Hristovski et al., 2003), our method only requires the user to specify the possible semantic relationships between the starting concept and the to-be-discovered target concepts rather than the possible semantic types of the target concepts and the bridge concepts. The purpose of selecting semantic types and semantic relations is to constraint the huge search space of the concepts collected from the downloaded documents. Bio-SbKDS not only significantly reduces the search space of the concepts, but also utilizes the possible semantic knowledge (i.e., semantic types, semantic relations and semantic hierarchy) on the bridge concepts and the target concepts to filter out those irrelevant concepts, spurious connections between the concepts.
Related work Swanson formalizes the procedure to discover undiscovered public knowledge from biomedical literatures as follows: consider two separate sets of literature CL and AL, where documents in CL discuss concept C and documents in AL discuss concept A. Each of these two sets of literatures discuss their relationship with some intermediate concepts B (also called bridge concepts), however, their possible connection via the
concepts B is not discussed together in any of these two groups of literature. The goal is to find some novel connections between the target Concepts A and starting concept. The connections or associations among the huge amount of the biomedical concepts grow exponentially. For example, in MeSH Metathesaurus, there are more than one million biomedical concepts. One of the challenging problems of Swanson’s method is how to find the truly novel hypotheses from the huge number of possible connections/relationships of the medical concepts in the literature. Srinivasan (2004), Joshi et al. (2004), Hristovski et al. (2003), and Pratt and Yetisgen-Yildiz (2003) have made significant progress on Swanson’s method. However, none of the approaches mentioned above consider the specific semantic relationships; those approaches used only the statistical measure for words or terms to select high informative words or terms without considering the semantic relations among concepts. However, the association problem should be tackled by not only the statistical measure but also the semantic information among the concepts. In contrast, we focus on developing fully automated approaches to this problem based on the semantic knowledge about the medical concepts and their relationships. We use semantic information to prune irrelevant medical concepts, bogus or non-interesting connections among the medical concepts. Our approach replaces manual ad-hoc pruning by using existing biomedical ontologies. Our use of an intermediate set of automated identified semantic types helps to manage the sizable branching factor. Our approach We discuss a system Bio-SbKDS to automatically uncover the undiscovered public knowledge from biomedical literature using a combination of ontological knowledge and data mining. We rely on biomedical ontologies, such as UMLS (http://umlsks.nlm. nih.gov) and MeSH (http://www.nlm.nih.gov/mesh/meshhome.html) to identify the biomedical concepts and their semantic types and semantic relationships among them. Bio-SbKDS generates much less connections to capture discoveries that are likely to be novel, and uses semantic knowledge to significantly reduce the search spaces in the discovery procedure. In order to create an automated and generalizable approach to identify those interesting and meaningful terms for the B and A concepts, we rely on semantic types (e.g., medical condition or disease and a potential treatment) that were plausible for terms that could be correlated, then Bio-SbKDS filter out any concepts that did not match the semantic-type criteria. Biomedical ontology MeSH Medical Subject Headings (MeSH), published by the National Library of Medicine (NLM) in 1954, consists of the controlled vocabulary (“Descriptors” in NLM’s term) and the thesaurus (“Entry terms” in NLM’s term) for its bibliographies and cataloging. During the process of indexing articles (after reading full versions), MeSH terms are assigned to each article. When MeSH terms are assigned to Medline documents, around three-five MeSH terms are set as “MajorTopic” which represent the document very well. We use MeSH terms from the Medline documents, instead of using the title words or UMLS concepts by detecting the terms in the titles or the abstracts through natural
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UMLS Unified Medical Language System (UMLS), started by NLM as a long-term R&D project in 1986, provides a mechanism for integrating all the major biomedical vocabularies including MeSH. Thus, using UMLS one can develop a computer system that “understands” the meaning of the language (National Library of Medicine, 2004). UMLS consists of three knowledge sources; Metathesaurus, Semantic Network, and SPECIALIST lexicon. Metathesaurus as a core is organized by concept (meaning), synonymous terms are clustered together to form a concept and concepts are linked to other concepts by means of various types of relationships to provide the various synonyms of the concept and to identify useful relationships between different concepts (National Library of Medicine, 2004). Currently, Metathesaurus (2004 AC version) contains more than one million biomedical concepts (meanings) and 4.5 million unique concept names from more than 100 different source vocabularies (National Library of Medicine, 2004). All concepts are assigned to at least one semantic type as a category. Currently, there are 135 semantic types. Each semantic type has at least one relationship with other semantic types. At this time of writing, there are 54 relations. Both the semantic types and semantic relations are hierarchically organized. Semantic relationships can be hierarchical (e.g., “isa”, “part of”) or associative (e.g., “treat-with-drug”, “interact-with”). Figure 1 shows the relationships of concepts, semantic types and semantic relations of Raynaud disease, blood viscosity and fish oils.
Figure 1. An illustrative example of the UMLS
The Algorithm Bio-SBKDS Input Starting concept C as MeSH term plus date range, initial semantic relations ISR between the starting concept and the to-be-discovered target concepts, the role of keyword for possible relations (subject or object). Output Target concept list (A concepts). Procedure . Step 1. Identify the semantic types of the starting concept C from the ontology UMLS; the result is denoted as ST_C. . Step 2. Find all the possible semantic types related ST_C; the semantic types derived are called ST_B_can (can means candidates) and are used as the category restriction for B concepts. . Step 3. Extract all semantic types related to ISR, which are the candidate semantic types for the to-be-discovered target concepts A, the result is denoted as ST_A_can. . Step 4. Extend ST_A_can obtained in step 3 by following through the ISA relations; the extended semantic types are called ST_A_can_ext. . Step 5. Check if there are relations between ST_B_can and ST_A_can_ext and also if the two semantic type sets pass the relation filter. If not, such semantic types are dropped from their semantic type list. After removing irrelevant semantic types, ST_B_can becomes ST_B and ST_A_can_ext becomes ST_A. . Step 6. Search the biomedical literature to get all the documents CL related to C; CL is the source of B concepts. Then, extract MeSH terms from CL; the terms are called B_can. . Step 7. Apply B concept category restriction (ST_B) to B; selecting the terms that only belong to at least one semantic type of ST_B. In addition, Bi-Decision Maker further qualifies B_can. Here, the top ranked B terms, called B_top, are selected. . Step 8. Search all B_top terms to get all the documents AL; AL is the source of the to-be discovered A concepts. Then, extract MeSH terms from AL; the terms are called A_can. . Step 9. Apply A concept category restriction (ST_A) to A_can. In addition, Bi-Decision Maker further qualifies A_can. . Step 10. From A_can, that are not co-occurred with C concept in Medline, the top ranked A concepts are selected. Below we explain each step in great details using the Raynaud disease as our example. Step 1. The semantic type of the starting concept C (ST_C) is identified through UMLS semantic network. At this time, only MeSH term is allowed as starting concept because the semantic type of the starting concept is used to construct the semantic type list for the B terms. For example, for the Raynaud disease, its semantic type is [Disease or Syndrome ].
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Step 2. All the semantic types (ST_B_can), which have at least one of the relations in the relation filters with ST_C (the semantic type of the keyword), are selected by considering the role of the initial keyword (i.e. as subject or as object). For example, in Table I [Physiologic Function ] and [Steroid ] are selected because the role of the initial keyword is set as an object on the interactive system and the relation filter includes “process_of”, “result_of”, and “causes”; just regard each record in Table I as a sentence (e.g. Steroid causes Disease or Syndrome). The relation filter between C and B are shown below: process_of result_of manifestation_of causes
The semantic types collected (ST_B_can) are used for the semantic types of B terms as category restriction. This is based on the fact that B terms have at least one relationship with C term. Step 3. In order to derive the semantic types of A terms, the initial semantic relations (e.g. “treats”, “prevents”) are used. Here, it is important that the C term is set as a subject or an object for the initial relations. If the term is set as an object, only the semantic types on the first (not third) column in Table II are considered in the search space. However, if a semantic type is too general, then that type is ignored. Whether a semantic type is “too general” or not is decided by its hierarchy level. Currently Level 1, 2, 3 (e.g. A1.4.1) in the UMLS semantic network are regarded as “too general” because the terms in the semantic types in such levels are too broad. Step 4. Extend the semantic types identified in step 3 by following through the ISA relations. Also “too general” semantic types are ignored. Actually through this process all sub-semantic types of the semantic types in step 3 are added to the semantic type list. For example, because [Antibiotic ] is sub-semantic type of [Pharmacologic Substance ], [Antibiotic ] is added. The four semantic types in step 3 are extended to 15 through this process as shown in Table III. These semantic types (ST_A_can_ext) are used for the semantic types of A terms as category restriction.
Table I. Semantic relations for some semantic types
Table II. Semantic relations for some semantic types
Semantic types (as subjects)
Relation
Semantic types (as objects)
Physiologic Function Physiologic Function Steroid
process_of result_of causes
Disease or Syndrome Disease or Syndrome Disease or Syndrome
Semantic types (as subjects)
Relation
Semantic types (as objects)
Antibiotic Drug Delivery Device Medical Device (too general) Pharmacologic Substance Therapeutic or Preventive Procedure
treats treats treats treats treats
Disease Disease Disease Disease Disease
or Syndrome or Syndrome or Syndrome or Syndrome or Syndrome
Step 5. Because there must exist at least one relationship between A terms and B terms, Bio-SbKDS should check if there is at least one relationship between ST_B (the semantic types for B concepts in step 2) and ST_A_can_ext (the semantic types for A concepts obtained in step 4). For example, there are no relationships for the three pairs in Table IV. First, for each semantic type for B terms Bio-SbKDS checks if there exist at least one relationship with any of the semantic types of A terms. If a semantic type for B terms does not have any relationship with any of the semantic types of A terms, the semantic type is dropped from the semantic type list of B terms. After this process is done with the semantic types of B terms, the same process is performed for the semantic types of A terms. These processes are called mutual qualification. During the mutual qualification procedure, Bio-SbKDS simultaneously checks if the two semantic type sets (for A terms and B terms) pass the predefined relation filter between A terms and B terms. This filter is shown below:
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interacts_with produces complicates
Table V shows the two semantic type sets for B concepts and A concepts that are automatically generated using only the initial relations and the relation filters. Step 6. In order to collect B term candidates, the starting concept C is searched against Medline. Here, we should consider what B terms should be. Because there should be some meaningful semantic relationships between B terms and C term (for B terms to be a bridge between A terms and C term), B terms should be the major topics (concepts) of the documents by the keyword searching against Medline. Therefore, we collect only MajorTopic MeSH terms from the downloaded documents and calculate their counts. The rationale to consider the counts of B candidates here is that we try to find something (as A concepts) that is strongly associated with C concepts. Step 7. B term category restriction, which consists of semantic types obtained in step 5, are applied to the MeSH terms extracted in step 6. Also “too general” MeSH terms are excluded. In addition to those qualifications, Bi-Decision Maker sub-system further qualifies the MeSH terms. The sub-system uses the definitions of MeSH terms
Drug Delivery Device Indicator, Reagent, or Diagnostic Aid Antibiotic Biologically Active Substance Pharmacologic Substance Chemical Viewed Functionally Immunologic Factor Receptor
Biomedical or Dental Material Therapeutic or Preventive Procedure Vitamin Hormone Enzyme Hazardous or Poisonous Substance Neuroreactive Substance or Biogenic Amine
Semantic types for B concepts
Semantic types for A concepts
Invertebrate Geographic Area Organic Chemical
Neuroreactive Substance or Biogenic Amine Neuroreactive Substance or Biogenic Amine Drug Delivery Device
Table III. The extended semantic types through tracking ISA relations
Table IV. The semantic types that have no relationship
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Semantic types as category restrictions for B concepts
Semantic types as category restrictions for A concepts
Indicator, Reagent, or Diagnostic Aid Antibiotic Biologically Active Substance Pharmacologic Substance Chemical Viewed Functionally Immunologic Factor Receptor Biomedical or Dental Material Therapeutic or Preventive Procedure Vitamin Hormone Enzyme Hazardous or Poisonous Substance Neuroreactive Substance or Biogenic Amine
Cell Function Carbohydrate Eicosanoid Steroid Mental or Behavioral Dysfunction Element, Ion, or Isotope Organophosphorus Compound Congenital Abnormality Amino Acid, Peptide, or Protein Organism Function Pathologic Function Organ or Tissue Function Chemical Viewed Structurally Nucleic Acid, Nucleoside, or Nucleotide Organic Chemical Cell or Molecular Dysfunction Inorganic Chemical Acquired Abnormality Molecular Function Neoplastic Process Mental Process Genetic Function Lipid Experimental Model of Disease Physiologic Function
Table V. The semantic types as category restrictions for B concepts and A concepts
to determine if the terms are appropriate to B concepts. After the qualification by Bi-Decision Maker, the top N terms are selected as B concepts (currently, N is 5). Table VI shows the top five B terms based on their counts for “Raynaud Disease – Fish Oils” case. “Blood Viscosity” is ranked in the first, which is the one Swanson found manually. Step 8. Unlike the initial search based on the starting concept C in step 6, the system searches all top B terms against Medline. The B terms are ranked by the counts of the terms. On searching, the same date range is used as the initial keyword. However, the documents, relevant to C concept should be excluded. Thus, the search keyword would be “B term AND Date_Range NOT C term”. Similar to step 6, only MajorTopic MeSH terms are collected. A sample keyword to search is the following: “Blood Viscosity”[MAJOR] 1960[dp]:1985[dp] NOT “Raynaud Disease”[MeSH]
Table VI. Top five bridge concepts with their counts
MajorTopic MeSH terms
Count
Blood Viscosity Quinazolines Pyridines Vinyl Chloride Imidazoles
22 10 8 8 8
Step 9. A term category restriction, which consists of the semantic types obtained in step 5, are applied to the MeSH terms extracted in step 8. Also “too general” MeSH terms are excluded. In addition to those qualifications, Bi-Decision Maker determines if the MeSH terms are appropriate to A concepts. Through these processes, A concept candidates are generated. Step 10. Because we try to find only novel C-A relationships, the system eliminates A candidates that already have some relationships with C concept by searching Medline; if C and A concepts co-occur together in the biomedical literature, those A concepts are dropped from the candidate list. From the A candidates, the top Na as A concepts are selected based on their weights from the B term. Table VII shows the experimental result of “Raynaud Disease – Fish Oils” problem with the different numbers of B concepts. Our results are very promising; our system finds out and ranks all the correct A concepts in 1st, and 5th. In other words, our model indicates Swanson’s “ABC” relationship can be precisely mined using semantic knowledge. The terms, bold and italic, are ones Swanson found manually.
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Conclusion This paper proposes a semantic based biomedical literature mining method for novel connections among the biomedical concepts. The method takes advantages of the biomedical ontologies, MeSH and UMLS, is designed to automatically uncover novel hypotheses/connections among relevant biomedical concepts. Using only the starting concept, and initial semantic relations, Bio-SbKDS can automatically generate the semantic types for B and A concepts. Using the semantic types as category restrictions and Bi-Decision Maker, Bio-SbKDS finds out A concepts through Medline search. There may be lots of ties of A concepts. The root cause of the problem is that the relationships are assigned in the semantic type level instead of the concept level in UMLS. Because a semantic type contains lots of concepts and the relationships are assigned in the semantic level, the relationships among concepts are inevitably obscure, ambiguous or equivocal. We will need to reduce and rank A concepts in a semantic manner, which would be very challenging. For this problem, we may need more disease specialized biomedical ontology, such as Systematized Nomenclature of Medicine (SNOMED) (http://www.snomed.org/). “Raynaud disease – Fish oils” problem Top 3 B concepts Blood Viscosity Quinazolines Pyridines Top 1 A concept Fish Oils
Top 5 B concepts Blood viscosity Quinazolines Pyridines Vinyl Chloride Imidazoles Top 5 A concepts “Anti-Inflammatory Agents, Non-Steroidal” Nicotine Niceritrol Antilipemic Agents Fish Oils
Table VII. Experiment results (no. of B ¼ 3 vs. no. of B ¼ 5)
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References DiGiacome, R.A., Kremer, J.M. and Shah, D.M. (1989), “Fish oil dietary supplementation in patients with Raynaud’s phenomenon: a double-blind, controlled, prospective study”, American Journal of Medicine, Vol. 8, pp. 158-64. Hristovski, D., Peterlin, B., Mitchell, J.A. and Humphrey, S.M. (2003), “Improving literature based discovery support by genetic knowledge integration”, Stud. Health Technol. Inform., Vol. 95, pp. 68-73. Joshi, R., Li, X.L., Ramachandaran, S. and Leong, T.Y. (2004), “Automatic model structuring from text using biomedical ontology”, American Association for Artificial Intelligence (AAAI) Workshop, San Jose, CA, pp. 74-9. Lindsay, R.K. and Gordon, M.D. (1999), “Literature-based discovery by lexical statistics”, Journal of the American Society for Information Science, Vol. 50 No. 7, pp. 574-87. National Library of Medicine (2004), AC UMLS Documentation, NLM, Bethesda, MD, available at: www.nlm.nih.gov/research/umls/documentation.html Pratt, W. and Yetisgen-Yildiz, M. (2003), “Litlinker: capturing connections across the biomedical literature”, K-CAP’03, Sanibel Island, FL, pp. 105-12. Srinivasan, P. (2004), “Text mining: generating hypotheses from MEDLINE”, Journal of the American Society for Information Science, Vol. 55 No. 4, pp. 396-413. Swanson, D.R. (1986a), “Undiscovered public knowledge”, Libr. Q, Vol. 56 No. 2, pp. 103-18. Swanson, D.R. (1986b), “Fish-oil, Raynaud’s Syndrome, and undiscovered public knowledge”, Perspectives in Biology and Medicine, Vol. 30 No. 1, pp. 7-18. Weeber, M., Vos, R., Klein, H., de Jong-Van den Berg, L.T.W., Aronson, A. and Molema, G. (2003), “Generating hypotheses by discovering implicit associations in the literature: a case report for new potential therapeutic uses for thalidomide”, Journal of the American Medical Informatics Association, Vol. 10 No. 3, pp. 252-9. Further reading Swanson, D.R. and Smalheiser, N.R. (1999), “Implicit text linkages between medline records: using Arrowsmith as an aid to scientific discovery”, Library Trends, Vol. 48 No. 1, pp. 48-59.
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A cooperative environment for the negotiation of term taxonomies in digital libraries Francesco Bellomi, Matteo Cristani and Roberta Cuel
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Dipartimento di Informatica, Universita` di Verona, Verona, Italy Abstract Purpose – Several systems for the management of digital libraries have evolved in the recent past from simple document repository to sophisticated applications that provide the possibility of classifying and, in some cases, even rating the documents collected in the library itself. In a few of those systems members of the community of practice that uses and produces the library itself can also be active in assigning a reputation score to each other. This study aimed to explore the idea of using a collectively negotiated term taxonomy in order to develop a better model for the automated evaluation of users’ competence on different discussion topics. Design/methodology/approach – The study provides a formalization of the approach we propose, and the proposal of an architecture for implementing a system deploying the approach. Findings – The study analyses the issues related to the implementation of a digital library, and develops an architecture that aims to evaluate users’ competence about different discussion topics in an automated way. Research limitations/implications – The paper only describes an abstract architecture of a self-balancing digital library. Further research should investigate the different possible choices for the implementation details that have been left out from this first explorative analysis. Originality/value – The novelty of the approach resides in the fact that we make use of a collectively negotiated taxonomy in order to automatically assign relevance scores to reviewers’ evaluations. Keywords Digital libraries, Knowledge management Paper type Research paper
1. Introduction Complex and networked organizations, structured into many, heterogeneous unities both internal or external and both formally (e.g., departments, divisions, national branches) and informally (e.g., communities, interest groups) need to cooperate and coordinate their work according to a more complex and dynamic value chain; to share knowledge both within the firm and across his network. In a lot of articles most KM projects share the goal of creating large and homogeneous knowledge-based systems (KBSs), in which corporate knowledge is made explicit, codified, organized according to a single and coherent schema, and then made available through an Enterprise The authors gratefully thank ACP s.r.l. for funding. This work has taken place within the projects DAVA “Tecniche di Decisione Automatica con criteri multipli e Valutazioni Arbitrarie” (“Techniques of automated decision with multiple criteria and arbitrary evaluation”), and DAVA 2. The authors also thank Creactive Consulting for funding. The work of the second and third authors have also taken place in a synergic effort with DAVA cooperator. The project name is “OASI (Ontologie degli Artefatti e dei Servizi alle imprese” – “Ontologies for the representation of artifacts and services in industrial applications”) and OASI 2
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Knowledge Portal (Davenport et al., 1998). This EKM allow effective documents and knowledge exchanging and coordination among organizations and organizational units. Moreover document repositories, databases, and digital libraries allow organizations to create a common artifact, a system of information (documents in different formats) and knowledge (the way in which documents are organized and managed) allow organization to reify the workers’ knowledge. The huge advantage of digital libraries come from the possibility of store official and unofficial documents, best practices, personal information, and allow workers to share knowledge according to their needs. In other words the digital library constitutes a good framework for communities. Workers, namely members of the community, share knowledge within a specific digital library, identify themselves as part of a community (therefore participating on that), and their point of view is well expressed in the digital library knowledge representation (document management system). For many applications (for example, document publication, sharing, and retrieval), the single schema is often represented by certain kind of classification, whose purpose is to provide a shared and objective conceptualizations of corporate knowledge, and to enable knowledge sharing across the organizational units. This classification is effected by the fact that what we know cannot be viewed simply as a “picture” of the world, as it always presupposes some degree of interpretation. Therefore every classification within a digital system is the result of a personal interpretation and contribution, an a social agreement on common terms. Problems arise because of no ideal language exists which simply depicts the world as it is. Depending on different interpretation schemas, people may use the same word with different meaning, or different words to mean the same thing; two groups of people may observes the same phenomenon, but still see different problems, different opportunities, and different challenges. For in depth analysis see how interpretation schemas is interpreted in various theories: paradigms (Kuhn 1979), frames (Goffamn, 1974), thought worlds (Dougherty, 1992), context (Ghidini and Giunchiglia, 2001), mental spaces (Fauconnier, 1985), cognitive path (Weick, 1979, 1993; Weick et al., 1999). This epistemological view, in which the explicit part of what we know gets its meaning from a (typically implicit, or taken for granted) interpretation schema leads to some important consequences regarding the use of classifications (and other conceptualizations) in KM systems. Indeed, it follows from what we said above, that a classification is not a neutral organization of a collection of items (e.g., documents in a database), but is the emergence of some interpretation schema, according to which it makes sense to classify things in that way. In short, a classification is always the result of a sense-making process, and represents the point of view of those who took part in that process (see Benerecetti et al. (2000) for a in-depth discussion of the dimensions along which any representation – including a classification – can vary depending on contextual factors). Personalized classification or more general representation systems can derive not only from psychological aspects of the single individual, but can be socially created. Social knowledge can be influenced by pragmatism or constructivism approaches, in which knowledge is defined by actions of the single worker. In a digital library these actions can be post a documents, evaluate a contribution, and become an expert in a specific domain. Through actions people affect the creation and the interpretation of knowledge. Moreover in the post-modernist approach knowledge is not a neutral asset of organization, it strongly depends on sentimental aspects of learning, and it is shared
through stories. The implicit part of concepts and knowledge is managed and shared through storytelling processes (Brown and Duguid, 1991), metaphors (Nonaka and Takuechi, 1995), and analogies which allow people to understand each other without explicit all the knowledge. In other words, people intuitively agree with the story, but interprets in a personalized way. This personalization is not autonomous but it is affected by the social structure in which we live (Giddens, 1984). More important is that the social structure is at the same time a barriers on our beliefs but at the same time is constructed and modified by individuals behaviors. For instance, consider a digital library focused on a specific topic. The topic of the digital library is at the same time the barrier to which people need to compare their work, and the representation of knowledge socially created and managed within the digital library. In recent investigations on digital libraries several scholars have argued that it is relevant to provide a shared model of the term taxonomy employed to classify the documents of the library itself. For an up-to-date viewpoint, containing relevant references see (Malizia et al., 2004). The opinion of the majority of the researchers in this field is that any authority approval of the taxonomy will encounter criticisms and disagreements in the community of practice using the library. The majority also agree that the solution should be achieved by means of member participation and that the nullification of the role of authoritative deployment of term taxonomy would not lead to a situation of total anarchy in the classification of documents. In the actual practice of authoritative deployment of term taxonomies, like, for instance, in Google’s Web Directory or keyword taxonomies of ACM or IEEE, or if, for instance, the chosen method is centralised and authoritative. A relevant question to be posed is: why do we trust an individual as authoritative in the definition of keywords which can be used to classify documents? The common sense answer, which almost everybody will give is: this individual is knowledgeable about this topic. In other terms, we assume that authority in keyword classification is strongly depending upon the reputation of the individual providing the classification. In fact, considering the communities of practice literature, the concept of peripheral participation is exactly based on reputation systems(Lave and Wenger, 1991). In particular a new comer make participation possible through peripheral and legitimacy processes. This means that, the participant in peripheries is legitimate to participate at the community’s activities, and is responsible of the community resources, and at the same time can use the communities resources and can produce new resources for other members. Even if she participate, she has a low level in responsibility and in reputation, and can grow her reputation actively contributing to the community. Considering the digital library environment, as a community. Newcomer could accede to the whole content of the library, might participate and post documents and comments, and their comments will be evaluated according her/his reputation and her/his position in the communities (more or less in the community’s peripheries). Moreover, the community of practice who employs the classification itself often approves the classification proposed by a single individual, or the amendments proposed by an individual, by means of a public discussion, which only in few cases can be formalized as a true polling system, but in fact assumes a form of a moderated discussion, in which the credibility of individuals making the proposals is as relevant as the one of those appreciating it. In other terms the communities of practice do not acknowledge a form of true democracy, but mediate between a system of formalized
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authoritative and a pure democratic system. Several arguments have been brought in this sense in the analyses about the evolution of science communities by several important scientists, even in the recent past, in particular focusing on the peer-reviewing system (Wenger, 1998). In digital libraries, the term taxonomy is the result of such a mediation, that very often is provided by means of a central moderation authority, for instance the web management teams, whose activity consists in deploying and amending the taxonomy itself as a result of an authoritative interpretation of common opinion in the community of practice using the library, that takes into account the credibility of the opinions expressed in the public discussions. In this paper we propose a system that deploys a fully distributed system of authoritative mediation based upon polling on taxonomy amendments. The term taxonomy structure has a natural resistance to the changes, and each change has a chance of being deployed which depends upon the reputation of those who voted for it. Conversely, the reputation of each user of the library depends upon the ability of the user of obtaining polls for the proposals of amendments he or she makes. The so called “web of trust” has been receiving a strong attention in the recent past, as well as digital libraries have. However, we should notice that only few cases exist in which the classification of documents has been provided by means of models not centralized and authoritative. We believe that this is due to the lack of an architecture proposal, under which we can provide analysis of the integrability of models. For a general reference to the recent achievements in the field of reputation assignment refer to (Resnick et al., 2000; Rana and Hinze, 2004). In the next two sections we articulate our discussion. In Section 2 we introduce the formalization of the model discussed in this section. Section 3 takes some conclusions and sketches further work. 2. A formal model Within a digital library, users publish documents on a variety of different topics, and express evaluations about their perceived quality of other users’ documents. The document management system autonomously infers the specific competency of an user, relative to a single given topic, by weighting each of his/her publication according to other users’ evaluations. In order to properly assess the contribution of each publication and third-part evaluation to the compute of competency, a notion of distance between topics is defined: the guiding principle is that a judgement expressed by an user on a given topic is as much relevant as his/her competency of the topic itself, as perceived by the community. The taxonomy of concepts negotiated within the community plays a fundamental role in the structure of the topics space, and thus in the definition of distance between topics. A digital library is an entity that is continuously changing, both in terms of the users involved and the content published. Since we want to track its evolution, we start by defining the notion of state of a digital library. A state L of a digital library is a five-tuple L ¼ ðU ; D; a; e; T) where: U
is the set of users of the library;
D
is the set of the documents in the library; typically, documents are mainly composed of natural language text;
a#U£D e : U £ D! ½0; 1R < fnilg
T
is the authorship relation: a(u,d) if and only if the user u is an author of the document d;
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is the evaluation function. Each user evaluates a subset of the documents in the library: e(u,d) is the evaluation of the document d expressed by the user u, in term of a real number ranging from 0 (most negative) to 1 (most positive), or nil if u chooses not to evaluate d; specifically, a user cannot evaluate his/her documents, so aðu; dÞ ! eðu; dÞ ¼ nil;
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is a couple ðC; hC Þ, where C is the set of concepts used to characterize the topics discussed in the documents and hc # C £ C characterizes the taxonomical structure of C; given c1 ; c2 [ C, we have hC ðc1 ; c2 Þ if C 1 is a hyponym of C 2 . hc is irreflexive, antisymmetric and transitive, i.e. a direct acyclic graph. We maintain that C is a subset of a set Q that includes all the admissible concepts. The actual characterization of Q is left to the specific implementation.
Let T be the space of topics. Given a taxonomy of concepts, it is possible to assign each document a topic, by means of a function T D : D £ T ! T . In general, we expect T D to be algorithmically computable. A distance dT is defined on the space of topics. A simple way to define T and T D is to borrow some ideas from statistical natural language processing techniques (SNLP). Within this approach, each concept could be defined by a collection of linguistic features, so that the set of mentioned concepts in each document could be automatically inferred. The topic space T could be defined as a boolean vector space, with a dimension for each concept. A topic is a vector that sums an orthogonal component for each concept, whose extent measure the degree of presence of the concept itself in the context (specifically, the document) taken into consideration. The Euclidean distance could be chosen as dT . However, this is only an example, and more refined or domain-specific approaches could be exploited. One of the limitations of this approach is that it loses the information on the way the use of the concepts is structured within each document; what we keep is only an unordered “bag of concepts”. We want to devise a characterization of the set of possible concepts Q that make it possible to evaluate algorithmically the presence of each concept in a document. Our proposal is to let users commit on the use of a common controlled vocabulary. A controlled vocabulary is a domain-specific lexicon of basic linguistic features (terms such as words and phrases), each of them used only with a commonly endorsed, informally specified sense. Given a document, it is trivial to test the text for the presence of a term of the controlled vocabulary. A concept c[ Q is characterized by a set of defining terms, chosen from the controlled vocabulary, and then used to test the presence of the concept in the documents of the library, by looking at the usage patterns of the defining terms themselves in the text of each document, by means of a suitably chosen heuristic procedure, such as one based on term weighting, term frequency counting, or the evaluation of a boolean expression defined on the occurrence of terms. It is important to notice that if a concept c1 subsumes a concept c2 it is not necessarily the case that the
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defining terms of c1 are also defining terms for c2 : defining terms are not inherently related to the concepts, but rather to the relationship between the concepts and an ideal “target corpus” where we want to recognize them. The “bag of concepts” vector space model mentioned above is plausible only if concepts are semantically disjoint; otherwise the axis of the topic space would not be orthogonal. This is clearly not the case of our hierarchical, taxonomic concept set. In order to correct this problem, we consider a vector space that assigns a dimension to each “leaf” concept in the taxonomy, and express the degree of presence of non-leaf concepts with a vector that sums components related to all its subsumed concepts. The function K : U £ T ! R express the competence of an user, relative to a specific topic. We expect K to be defined in order to satisfy the following informal constraints: . the value of K(u,t) is derived from the documents Du ¼ {djaðu; dÞ}; . the weight of the contribution of each document d in Du is derived from dT ðT D ðdÞ; tÞ; . the weight of the contribution of each document d in Du is derived from its evaluations; and . the weight of the contribution of each evaluation eðui ; dÞ is derived from Kðui ; dÞ. Within this system, there are two mutually interconnected sources of authority: the taxonomy of concepts (as defined by C and hC ), and the users’ evaluations (as defined by e). The space of topics based upon reputation is presented in Figure 1, whilst the reputation assignment is represented in Figure 2. The taxonomy of concepts can be changed using a protocol based on polling. Let P be the set of possible change proposal. When a change proposal p is accepted and
Figure 1. The assignment of topics
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Figure 2. The assignment of reputation
applied to a taxonomy T 0 ,the taxonomy is updated, resulting in a new taxonomy T 1 . We use the notation T 0 p ¼ T 1 to refer to the update operation. A change proposal is defined by a sequence of atomic changes. The set AC T of the possible atomic change proposals on the taxonomy TðC; hC Þ is the smallest set such that: . c [ Q; c C ! add T ðcÞ [ AC T (concept addition); . c [ C ! del T ðcÞ [ AC T (concept deletion); . c1 ; c2 [ C; ðc1 ; c2 Þ hc ! add hT ðc1 ; c2 Þ [ AC T , provided that hc < ðc1 ; c2 Þ is still a DAG (hyponymical link addition); . c1 ; c2 [ C; ðc1 ; c2 Þ [ hC ! del hT ðc1 ; c2 Þ [ AC T (hyponymical link removal). The set C of change proposals is the defined as: C ¼
