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 1.State of Web search Picked the low hanging fruit –Heavy investments, marginal returns –High hanging fruits Hard searches remain… 2.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 eiffel url1 url3 url2 paris

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

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 Semantic Web http://technorati.com/posts/tag/semanticweb

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

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. Making the Web searchable Encourage the emergence of ontologies, the creation of metadata Support standards for the discovery of embedded metadata and the querying of ontology stores Harvest and actively use user-generated metadata

27. 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”

28. 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.

29. The open data movement Public money, public information User generated content, owned by the community Personal information, owned by the user + the law cannot follow anyway

30. Caveats 99% of the Web is Web 1.0 –And will stay like that Market leaders will not join the standards –Have most to loose, can achieve the same effect through proprietary agreements with large data providers –Technological rational also dictates that it’s likely to break through in domain-specific search engines

31. Examples Swoogle, SWSE Freebase dbpedia pediaX Technorati microformat search Semantic Wiki Foafing the music