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Peter Mika's Presentation at SSSW 2011

  1. Semantic Search Peter Mika Yahoo! Research
  2. Yahoo! serves over 680 million users in 25 countries
  3. Yahoo! Research: visit us at
  4. Search is really fast, without necessarily being intelligent
  5. Dealing with sparse collections Note: don’t solve the sparsity problem where it doesn’t exist
  6. Contextual Search: content-based recommendations Hovering over an underlined phrase triggers a search for related news items.
  7. Contextual Search: personalization Machine Learning based ‘search’ algorithm selects the main story and the three alternate stories based on the users demographics (age, gender etc.) and previous behavior. Display advertizing is a similar top-1 search problem on the collection of advertisements.
  8. Contextual Search: new devices Show related content Connect to friends watching the same
  9. Aggregation across different dimensions Hyperlocal: showing content from across Yahoo that is relevant to a particular neighbourhood.
  10. Direct answers in search Information box with content from and links to Yahoo! Travel Points of interest in Vienna, Austria Since Aug, 2010, ‘regular’ search results are ‘Powered by Bing’ Products from Yahoo! Shopping
  11. This is not a business model
  12. Semantic Search
  13. Semantics at every step of the IR process bla bla bla? q=“bla” * 3 Document processing bla bla bla Indexing Ranking Query interpretation Result presentation The IR engine The Web bla bla bla bla bla bla “ bla” θ (q,d)
  14. Data on the Web
  15. Linked Data: interlinked RDF documents example:roi “ Roi Blanco” name foaf:Person sameAs example:roi2 worksWith example:peter “” email type type Roi’s homepage Yahoo Friend-of-a-Friend ontology
  16. RDFa: metadata embedded in HTML … <p typeof=”foaf:Person&quot; about=&quot;;> <span property=”foaf:name” >Roi Blanco</span>. <a rel=”owl:sameAs&quot; href=&quot;;> Roi Blanco </a>. You can contact him at <a rel=”foaf:mbox&quot; href=&quot;;> via email </ a>. </p> ... Roi’s homepage
  17. Query Interpretation
  18. Indexing and Ranking
  19. Hosted by Yahoo! Labs at
  20. Evaluation form
  21. Evaluation form
  22. Search interface
  23. Example: Yahoo! Enhanced Results Enhanced result with deep links, rating, address.
  24. Example: Yahoo! Vertical Intent Search Related actors and movies
  25. Example: Semantic Information Mashup (DERI)

Hinweis der Redaktion

  1. In fact, some of these searches are so hard that the users don’t even try them anymore
  2. With ads, the situation is even worse due to the sparsity problem. Note how poor the ads are…
  3. Search is a form of content aggregation
  4. Semantic search can be seen as a retrieval paradigm Centered on the use of semantics Incorporates the semantics entailed by the query and (or) the resources into the matching process, it essentially performs semantic search.
  5. Bar celona
  6. Close to the topic of keyword-search in databases, except knowledge-bases have a schema-oblivious design Different papers assume vastly different query needs even on the same type of data