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Making the Web searchable, or the Future of Web Search

Making the Web searchable, or the Future of Web Search. Peter Mika Yahoo! Research Barcelona. Overview. Why a new vision? Context Semantic Web: metadata infrastructure Web 2.0: user-generated metadata Thesis: making the Web searchable Research challenges (SW & IR) Conclusion. Motivation.

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Making the Web searchable, or the Future of Web Search

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  1. Making the Web searchable, or the Future of Web Search Peter Mika Yahoo! Research Barcelona

  2. Overview • Why a new vision? • Context • Semantic Web: metadata infrastructure • Web 2.0: user-generated metadata • Thesis: making the Web searchable • Research challenges (SW & IR) • Conclusion

  3. Motivation • State of Web search • Picked the low hanging fruit • Heavy investments, marginal returns • High hanging fruits • Hard searches remain… • The Web has changed…

  4. Hard searches • Ambiguous searches • Paris Hilton • Multimedia search • Images of Paris Hilton • Imprecise or overly precise searches • Publications by Jim Hendler • Find images of strong and adventurous people (Lenat) • Searches for descriptions • Search for yourself without using your name • Product search (ads!) • Searches that require aggregation • Size of the Eiffer tower (Lenat) • Public opinion on Britney Spears • Queries that require a deeper understanding of the query, the content and/or the world at large • Note: some of these are so hard that users don’t even try them any more

  5. Example…

  6. The Semantic Web (1996-…) • Making the content of the Web machine processable through metadata • Documents, databases, Web services • Active research, standardization, startups • Ontology languages (RDF, OWL family), query language for RDF (SPARQL) • Software support (metadata stores, reasoners, APIs)

  7. Problem: difficulties in deployment • Not enough take-up in the Web community at large • Technological challenges • Discovery • Ontology learning • Ontology mapping • Lack of attention to the social side • Over-estimating complexity for users • Need for supporting ontology creation and sharing • Focus shifts from documents to databases --the Web of Data • Enterprise/closed community applications

  8. Web 2.0 (2003-) • Simple, nimble, socially transparent interfaces • Simplified KR • e.g. tagging, microformats, Wikipedia infoboxes • In exchange for a better experience, users are willing to • Provide content, markup and metadata • Provide data on themselves and their networks • Rank, rate, filter, forward • Develop software and improve your site • …

  9. Problem: lack of foundations • No shared syntax or semantics • No linking mechanism • Example: tag semantics • flickr:ajax = del.icio.us:ajax ? • flickr:ajax:Peter = flickr:ajax:John ? • flickr:ajax:Peter:1990 = flickr:ajax:Peter:2006? • Microformats • Separate agreement required for each format

  10. Thesis: making the Web searchable • The Web has changed • Content owners are interested in their content to be found (Web 2.0) • Cf. findability (Peter Morville), reusability (mashups), open data movement • Foundations are laid for a Semantic Web • We need to • Combine the best of Web 2.0 and the Semantic Web • Reconsider Web IR in this new world

  11. Semantic Web 2.0 • Getting the representation right • RDF++ • RDFa (RDF-in-HTML) • Innovations on the interface side • Semantic Wikis • New methods of reasoning • Semantics = syntax + statistics • Bottom-up, emergent semantics • Methods of logical reasoning combined with methods of graph mining, statistics • Scalability • Giving up soundness and/or completeness • Dealing with the mess • Social engineering • Collaborative spaces for creating and sharing ontologies, data • Connecting islands of semantics • Best practices, documentation, advocacy

  12. Example: Freebase

  13. Example: machine tags

  14. Example: folksonomies • Simplified view: “tags are just anchortext” • Can be used to generate simple co-occurrence graphs hilton url1 paris url2 eiffel url3

  15. A A B B

  16. The more complete picture • Folksonomies as tripartite graphs of users, urls and tags user1 user2 hilton url1 paris user3 url2 eiffel url3

  17. Community-based ontology mining • Opportunities for mining community-specific interpretations of the world • Peter Mika. Ontologies are us: A unified model of social networks and semantics.Journal of Web Semantics 5 (1), page 5-15, 2007

  18. Web IR 2.0 • Keep on improving machine technology • NLP • Information Extraction • Exploit the users for the tasks that are hard for the machine • Encourage and support users • Exploit user-generated metadata in any shape or form • Support standards of the SW architecture

  19. Vision: ontology-based search • Query: at the knowledge level • Partial description of a class/instance • Mapping of queries and resources in the conceptual space • Computing relevance in semantic terms • Novel user interfaces

  20. Ideal world • Plenty of precise metadata to harvest • User intent can be captured directly as a SPARQL query • Single ontology used both by the query and the knowledge base • Executed on a single knowledge base, gives the correct, single answer

  21. Technical challenges • Query interface • Data quality • Cleaning up metadata, tags • Spam • Ontology mapping and entity resolution • Ranking across types • Results display • How do you avoid information overload? • How do you display information you partially understand?

  22. Social challenges • Getting the users on your side • Users are unwilling to submit large amounts of structured data to a commercial entity (Google Base) • Provide a clear motivation and/or instant gratification • Trust them… but not too much (Mahalo)

  23. Example: Technorati and microformats <a href="http://technorati.com/tag/semweb" rel="tag">Semantic Web</a> http://technorati.com/posts/tag/semanticweb

  24. Example: openacademia.org and RDFa <span class="foaf:Person" property="foaf:name" about="#peter_mika"> Peter Mika </span>

  25. Conclusion • Why a new vision? • The opportunity: convergence • Semantic Web: metadata infrastructure • Web 2.0: user-generated metadata • Thesis: making the Web searchable • Research challenges

  26. What is there to gain? • Knowledge-based search • Sorting out hard searches • Creating new information needs • Beyond search • Analysis, design, diagnosis etc. on top of aggregated data • Personalization • Rich user profiles • Monetization • No more “buy virgins on eBay”

  27. Questions? • Peter Mika. Social Networks and the Semantic Web. Springer, July, 2007. • Special Issue on the Semantic Web and Web 2.0, Journal of Web Semantics, December, 2007.

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