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Introduction to Semantic Web in Library Services

Introduction to Semantic Web in Library Services. Dr. Devika P. Madalli Documentation Research and Training Center Indian Statistical Institute, Bangalore. Introduction. World Wide Web has emerged as a global medium for information exchange after the advent of Internet.

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Introduction to Semantic Web in Library Services

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  1. Introduction to Semantic Web in Library Services Dr. Devika P. MadalliDocumentation Research and Training Center Indian Statistical Institute, Bangalore

  2. Introduction • World Wide Web has emerged as a global medium for information exchange after the advent of Internet. • As the technologies evolved, WWW became more dynamic and responsive than being merely a static collection of web documents. • As business and service sectors grew, the potential of Web, various standards, software components written in different programming languages were deployed. • Thus, Web service technology has introduced a new abstraction layer over and a radically new architecture for software, setting the stage to grow exponentially to handle complex web services (Sabou, 2006).

  3. Definition • Semantic Web is a group of methods and technologies to allow machines to understand the meaning - or "semantics" - of information on the World Wide Web (Wikipedia, 2011)

  4. WWW to Semantic Web • As an innovative concept, Semantic Web, develops techniques to use the existing Web data with logics based formal descriptions of their meaning. Here ontologies came into play (Gruber, 1993).

  5. Web to Semantic Web • Majority of the web pages (static) are written in HTML • Even the dynamic web pages wrap information in HTML • Though dynamic web pages are retrieved normally from structured databases, but they become unstructured in HTML. In any case dynamic pages are not indexed by search engines. (Deep web problem)

  6. Web to Semantic Web • HTML is more a word processor of the web not a database of the web • HTML Tags are non-semantic • For eg: • <html> </html> • <title> </title> • <body> • <p> • </body>

  7. Semantics • Machine can handle structured data (XML) but not unstructured data (HTML) • Presently, only humans can handle unstructured data • Eg: you have a tooth problem can your web agent recommend a “dentist” to you?

  8. In essence • Problem: Much of the data/information on the web is meant for human understanding and not machine processable. • Challenge: How to make data machine processable • One solution: metadata and ontologies (Librarians' tools)

  9. Library Vs Semantic Web • Given that the library and the Semantic Web are cultures devoted to increasing information access and knowledge discovery, it makes sense to explore the foundations of the library (the more established institution) and consider what primary functions may help advance the Semantic Web initiative (Greenberg , 2007).

  10. Similarities (Greenberg, 2007)

  11. Semantic Web Development

  12. Library Approach • Compare Web Search Engines with Search facilities librarians are familiar with (bibliographic databases), like • CD-databases • On-line databases • Library automation packages

  13. Different Search options • Nested Boolean • By Field • By Date • By Range • Proximity

  14. Context Sensitive Search • Can we do Context sensitive search? • LIS has many models • PRECIS • POPSI etc. • Are we overemphasising on Recall? • In the era of 'information glut/deluge', should we emphasize recall rather than Precision?

  15. DRTC-ISI Semantic Web projects Living Knowledge: European Commission project, Frontier and Emerging Technologies (FET) AgINFRA: European Commission project, e-infrastructure project

  16. Background – LivingKnowledge Project Living Knowledge’ (LK) [EU FET project n0 231126] considers diversity as an asset and aimed to make it traceable, understandable and exploitable, with the goal of improving navigation and searching in very large datasets (Maltese, etal, 2009). Aims of the project study the effects of diversity and time on opinions and bias in socio-economic relevance, especially for seamless representation and exchange of information. Intuitive search and navigation tools (e.g. search engines) need produce more insightful, better organized, aggregated and easier-to-understand output.

  17. Living Knowledge Consortium 1. UNIVERSITÀ DEGLI STUDI DI TRENTO, Trento  - ITALY 2. FUNDACIÓ BARCELONA MEDIA UNIVERSITAT POMPEU FABRA, Barcelona – SPAIN 3. SORA, Vienna – AUSTRIA 4. CONSORZIO NAZIONALE INTERUNIVERSITARIO PER LE TELECOMUNICAZIONI, Parma  ITALY 5. STICHTING EUROPEAN ARCHIVE, Amsterdam – NETHERLANDS 6. UNIVERSITÀ DEGLI STUDI DI PAVIA, Pavia – ITALY 7. UNIVERSITY OF SOUTHAMPTON, Southampton,  UNITED KINDOM 8. DOCUMENTATION RESEARCH AND TRAINING CENTRE, INDIAN STATISTICAL INSTITUTE 9. GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER, GERMANY. 10. MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V., Muenchen – GERMANY

  18. Living Knowledge Project

  19. Aginfra European Commission FP7 'research infrastructure...' project http://aginfra.eu/

  20. Aginfra A data infrastructure to support agricultural scientific communities Promoting data sharing and development of trust in agricultural sciences

  21. AgInfra Consortium • University of Alcala (UAH), Spain • Food & Agriculture Organization of the United Nations (FAO), Rome , Italy • National Institute of Nuclear Physics (INFN), Italy • Salzburg Research Forschungsgesellschaft (SRFG), Austria • Institute of Physics, Belgrade (IPB), Serbia • Computer and Automation Research Institute, Hungarian Academy of Sciences (SZTAKI), Hungary • Agro-Know Technologies (AK), Greece • 21c Consultancy (21c), UK • Escuela Superior Politecnica del Litoral (ESPOL), Ecuador • Chinese Academy of Agricultural Sciences (CAAS), China • The Open University (OU), UK • Indian Statistical Institute, India

  22. Glimpses of music ontology Entity type [E] Musical instrument Idophone Struck idiophone Plucked idophone Friction idophone Membranophone Struck membranophone Plucked membranophone Friction membranophone Singing membrane Chordophone Simple chordophone Composite chordphone Aerophone Free aerophone Non-free aerophone Electrophone Entity type [E] Kinds of music Dramatic music opera Religious music Church music Sacred instrumental music Vocal music Sequences Capella music Instrumental music Symphonic music Ensemble music Popular music Avant garde Chamber music Instruments concertante

  23. Glimpses of music ontology (2) Attribute [A] Musical work First movement Allegro Presto Second movement Third movement Last movement Music form Shorter form Dance form Ballroom dance Media Utilities Storage media Compression File format Standard and quality Ceritification Certification ... Relation [R] Person Study Musicologist Organologist Ethnomusicologist Instrument Pianist Violinist Keyboardist Contribution Writer Vocalist Lyricist Work Impresario Choral director Arranger Recording Recording engineer Audio-visual technician

  24. Statistics

  25. Can we? • Can we get precise search results for queries like • Who is the author of Tom Sawyer? • Who works on ontology engineering in India? • I have toothache! (fetches list of dentists) • What are the trains between Mumbai and Delhi?

  26. Challenges for Semantic Web • Knowledge modelling • Domain Ontology Building and Inconsistent Ontologies • Crosswalking • Interoperability

  27. Semantic Technology for Libraries • Richer metadata • Enhanced user-profiling • Enhanced searching and browsing • Displaying results • Connecting ideas and people

  28. References • McIlraith, S., Son, T., and Zeng, H. (2001). Semantic Web Services. IEEE Intelligent Systems. Special Issue on the Semantic Web, 16(2):46 – 53. • Sabou, M. (2006). Building Web Service Ontologies, SIKS Dissertation Series No. 2006-4. • Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The Semantic Web. Scientific American, 284(5): 34-43. • http://xmlns.com/foaf/spec/

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