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Principles and pragmatics of a Semantic Culture Web

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Invited talk at meeting of Europeana national libraries, Frankfurt, 31 January 2008

Publicado en: Tecnología
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Principles and pragmatics of a Semantic Culture Web

  1. 1. Principles andPrinciples and pragmatics of apragmatics of a Semantic Culture WebSemantic Culture Web Tearing down walls and Building bridges
  2. 2. • Part of large Dutch knowledge-economy project MultimediaN • Partners: VU, CWI, UvA, DEN,ICN • People: Alia Amin, Lora Aroyo, Mark van Assem, Victor de Boer, Lynda Hardman, Michiel Hildebrand, Laura Hollink, Marco de Niet, Borys Omelayenko, Marie-France van Orsouw, Jacco van Ossenbruggen, Guus Schreiber Jos Taekema, Annemiek Teesing, Anna Tordai, Jan Wielemaker, Bob Wielinga • Artchive.com, Rijksmuseum Amsterdam, Dutch ethnology musea (Amsterdam, Leiden), National Library (Bibliopolis) Acknowledgements
  3. 3. The Web: resources and links URL URL Web link
  4. 4. The Semantic Web: typed resources and links URL URL Web link ULAN Henri Matisse Dublin Core creator Painting “Woman with hat SFMOMA
  5. 5. Principle 1: semantic annotation • Description of web objects with “concepts” from a shared vocabulary
  6. 6. Principle 2: semantic search • Search for objects which are linked via concepts (semantic link) • Use the type of semantic link to provide meaningful presentation of the search results Paris Montmartre PartOf Query “Paris”
  7. 7. The myth of a unified vocabulary • In large virtual collections there are always multiple vocabularies – In multiple languages • Every vocabulary has its own perspective – You can’t just merge them • But you can use vocabularies jointly by defining a limited set of links – “Vocabulary alignment” • It is surprising what you can do with just a few links
  8. 8. Principle 3: vocabulary alignment “Tokugawa” SVCN period Edo SVCN is local in-house ethnology thesaurus AAT style/period Edo (Japanese period) Tokugawa AAT is Getty’s Art & Architecture Thesaurus
  9. 9. A link between two thesauri
  10. 10. Levels of interoperability • Syntactic interoperability – using data formats that you can share – XML family is the preferred option • Semantic interoperability – How to share meaning / concepts – Technology for finding and representing semantic links
  11. 11. Cross collection search http://e-culture.multimedian.nl
  12. 12. Term disambiguation is key issue in semantic search • Post-query – Sort search results based on different meanings of the search term – Mimics Google-type search • Pre-query – Ask user to disambiguate by displaying list of possible meanings – Interface is more complex, but more search functionality can be offered
  13. 13. Keyword search with semantic clustering 1. Btree of literals plus Porter stem and metaphone index 2. Find resources with matching labels • Default resources are “Work”s 3. Find related resources by one-way graph traversal • owl:inverseOf is used • Threshold used for constraining search 4. Cluster results (group instances)
  14. 14. Faceted search Faceted search
  15. 15. What do you need to do to make your collection part of a Semantic Culture Web? Four activities
  16. 16. From metadata to semantic metadata 1. Make vocabulary interoperable 2. Align metadata schema 3. Enrich metadata 4. Align vocabulary
  17. 17. Activity 1: making vocabulary interoperabie • Make your vocabularies available in the Web standard RDF • Many organizations are already do this • W3C provides the SKOS template to make this almost straightforward • Effort required: at most a few days
  18. 18. Activity 2: aligning the metadata schema • Specify your collection metadata scheme as a specialization of Dublin Core • With RDF/OWL this is easy/trivial! • Cf. DC Application Profiles
  19. 19. Aligning VRA with Dublin Core • VRA is specialization of Dublin Core for visual resources • VRA properties “material.medium” and “material.support” are specializations of Dublin Core property “format” vra:material.medium rdfs:subPropertyOf dc:fotmat . vra:material.medium rdfs:subPropertyOf dc:format .
  20. 20. Activity 3: enriching the metadata • Extracting additional concepts from an annotation – Matching the string “Paris” to a vocabulary term • Information-extraction techniques exists (and continue to be developed) • Effort required can be up to a few weeks – The more concepts, the better, but no need to be perfect!
  21. 21. Example textual annotation
  22. 22. Resulting semantic annotation (rendered as HTML with RDFa)
  23. 23. Activity 4: aligning the vocabulary • Find semantic links between vocabulary links – Derain (ULAN) related-to Fauve (AAT)) • Automatic techniques exists, but performance varies • Often combination of automatic and manual alignment • Effort strongly dependent on vocabularies – But “a little semantic goes a long way” (Hendler)
  24. 24. Learning alignments • Learning relations between art styles in AAT and artists in ULAN through NLP of art historic texts – “Who are Impressionist painters?”
  25. 25. Extracting additional knowledge from scope notes
  26. 26. Perspectives • Basic Semantic Web technology is ready for deployment • Web 2.0 facilities fit well: – Involving community experts in annotation – Personalization, myArt • Social barriers have to be overcome! – “open door” policy – Involvement of general public => issues of “quality”
  27. 27. Planned: annotation interfaces • Brings in issues w.r.t. trust and access control – Quality issue • Involving users in the annotation process – Stimulating people to contribute • User profiling
  28. 28. Planned: large-scale deployment • Moving to 100-150M triples, 30+ collections • Scalability is key – Load balancing, 32+Gb RAM – Search algorithms • Methodology for including new collections • Continuous user and usability studies

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