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Semantic Search Peter Mika  Yahoo! Research
Yahoo! serves over 680 million users in 25 countries
Yahoo! Research: visit us at research.yahoo.com
Yahoo! Research Barcelona ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Search is really fast, without necessarily being intelligent
Why Semantic Search? Part I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Poorly solved information needs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Many of these queries would not be asked by users, who learned over time what search technology can and can not do.
Dealing with sparse collections Note: don’t solve the sparsity problem where it doesn’t exist
Contextual Search: content-based recommendations Hovering over an underlined phrase triggers a search for related news items.
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.
Contextual Search: new devices Show related content Connect to friends watching the same
Aggregation across different dimensions Hyperlocal: showing content from across Yahoo that is relevant to a particular neighbourhood.
Why Semantic Search? Part II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
Novel search tasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Semantic Search? Part III ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
This is not a business model http://en.wikipedia.org/wiki/Underpants_Gnomes
Example: rNews ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Facebook’s Like and the Open Graph Protocol ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Facebook’s Open Graph Protocol ,[object Object],[object Object],[object Object],[object Object],[object Object],<html  xmlns:og=&quot;http://opengraphprotocol.org/schema/&quot; >  <head>  <title>The Rock (1996)</title>  <meta  property=&quot;og:title&quot;  content=&quot;The Rock&quot; />  <meta  property=&quot;og:type&quot;  content=&quot;movie&quot; />  <meta  property=&quot;og:url&quot;  content=&quot;http://www.imdb.com/title/tt0117500/&quot; />  <meta  property=&quot;og:image&quot;  content=&quot;http://ia.media-imdb.com/images/rock.jpg&quot; /> … </head> ...
Semantic Search
Semantic Search: a definition  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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)
Data on the Web
Data on the Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Extraction methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Resource Description Framework (RDF) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],example:roi “ Roi Blanco” name type foaf:Person RDF document
Linked Data: interlinked RDF documents example:roi “ Roi Blanco” name foaf:Person sameAs example:roi2 worksWith example:peter “ pmika@yahoo-inc.com” email type type Roi’s homepage Yahoo Friend-of-a-Friend ontology
RDFa: metadata embedded in HTML … <p  typeof=”foaf:Person&quot;  about=&quot;http://example.org/roi&quot;>  <span  property=”foaf:name” >Roi Blanco</span>.  <a  rel=”owl:sameAs&quot;  href=&quot;http://research.yahoo.com/roi&quot;>  Roi Blanco  </a>.  You can contact him at  <a  rel=”foaf:mbox&quot;  href=&quot;mailto:roi@yahoo-inc.com&quot;>  via email </ a>.  </p> ...  Roi’s homepage
Crawling the Semantic Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data fusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data quality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The role of ontologies in semantic search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Query Interpretation
Query Interpretation in Information Retrieval ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Query interpretation in Semantic Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Indexing and Ranking
Indexing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DB-style indexing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IR-style indexing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Horizontal index structure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Vertical index structure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation ,[object Object]
Semantic Search evaluation  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hosted by Yahoo! Labs at semsearch.yahoo.com
Assessment with Amazon Mechanical Turk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Number of tasks completed per worker (2010)
Evaluation form
Evaluation form
Catching the bad guys ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Worker Known bad Real Known Good Total  N Time to complete (sec) N Mean N Mean N Mean badguy 20 2.556 200 2.738 20 2.684 240 29.6 goodguy 13 1 130 2.038 13 3 156 95 whoknows 1 1 21 1.571 2 3 24 83.5
Lessons learned ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Search interface
Search Interface ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Snippet generation using metadata ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Yahoo! Enhanced Results Enhanced result with deep links, rating, address.
Example: Yahoo! Vertical Intent Search Related actors and movies
Example: Sig.ma Semantic Information Mashup (DERI)
Future work in Semantic Web Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The End ,[object Object],[object Object],[object Object],[object Object]

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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 research.yahoo.com
  • 4.
  • 5. Search is really fast, without necessarily being intelligent
  • 6.
  • 7.
  • 8. Dealing with sparse collections Note: don’t solve the sparsity problem where it doesn’t exist
  • 9. Contextual Search: content-based recommendations Hovering over an underlined phrase triggers a search for related news items.
  • 10. 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.
  • 11. Contextual Search: new devices Show related content Connect to friends watching the same
  • 12. Aggregation across different dimensions Hyperlocal: showing content from across Yahoo that is relevant to a particular neighbourhood.
  • 13.
  • 14. 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
  • 15.
  • 16.
  • 17. This is not a business model http://en.wikipedia.org/wiki/Underpants_Gnomes
  • 18.
  • 19.
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  • 23. 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)
  • 24. Data on the Web
  • 25.
  • 26.
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  • 28.
  • 29. Linked Data: interlinked RDF documents example:roi “ Roi Blanco” name foaf:Person sameAs example:roi2 worksWith example:peter “ pmika@yahoo-inc.com” email type type Roi’s homepage Yahoo Friend-of-a-Friend ontology
  • 30. RDFa: metadata embedded in HTML … <p typeof=”foaf:Person&quot; about=&quot;http://example.org/roi&quot;> <span property=”foaf:name” >Roi Blanco</span>. <a rel=”owl:sameAs&quot; href=&quot;http://research.yahoo.com/roi&quot;> Roi Blanco </a>. You can contact him at <a rel=”foaf:mbox&quot; href=&quot;mailto:roi@yahoo-inc.com&quot;> via email </ a>. </p> ... Roi’s homepage
  • 31.
  • 32.
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  • 36.
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  • 39.
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  • 47. Hosted by Yahoo! Labs at semsearch.yahoo.com
  • 48.
  • 51.
  • 52.
  • 53.
  • 55.
  • 56.
  • 57. Example: Yahoo! Enhanced Results Enhanced result with deep links, rating, address.
  • 58. Example: Yahoo! Vertical Intent Search Related actors and movies
  • 59. Example: Sig.ma Semantic Information Mashup (DERI)
  • 60.
  • 61.

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