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Knowledge Server

Knowledge Server. Enhancing the Performance of Web Search Engines. Requirements for enhancing the Performance of search engines. Requirements for enhancing the Performance of search engines. Requirements for enhancing the Performance of search engines. Be able to understand the user’s query

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Knowledge Server

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  1. Knowledge Server Enhancing the Performance of Web Search Engines

  2. Requirementsfor enhancing the Performance of search engines • Requirements • for enhancing the Performance of search engines April 2006 - Building the knowledge

  3. Requirementsfor enhancing the Performance of search engines • Be able to understand the user’s query • to restrict the number of results • to propose better results • Be able to make suggestions • to enlarge the search • to limit the search • to propose focused ad’ or services • Be independent of languages • especially for language oriented engines • Ex: search in german is more complicated • Be able to profile the user • especially for thematic search engines April 2006 - Building the knowledge

  4. Knowledge Serverwhat it’s all about • Knowledge Server • what it’s all about April 2006 - Building the knowledge

  5. Knowledge Server :what it’s all about • A natural Engine • A powerful, unique Servo-database • A new way to design intelligent search applications April 2006 - Building the knowledge

  6. Knowledge Server :a neuromimetic engine • A Neuromimetic Engine • A powerful, unique Servo-database • A new way to design intelligent search applications April 2006 - Building the knowledge

  7. Knowledge Server : a neuromimetic engine • Never-ending, language independent learning • Neuromimetic Reading-Head • Automatic understanding of language • Dynamic hierarchisation of internal knowledge (shapes + associations) April 2006 - Building the knowledge

  8. Knowledge Server : a neuromimetic engine (1) • Never-ending, language independent learning • of shapes • of associations between shapes • Neuromimetic Reading-Head • non-linear reading process April 2006 - Building the knowledge

  9. Knowledge Server a neuromimetic engine (2) • Automatic understanding of language • phrases (non-limited in length) • atoms in aggregates (important e.g: in German) • order of words (important e.g: in French) • case of words ("Web" = www; "web" = animal) • accents (important e.g: in French) • liaison words (e.g: "the", "and", etc.) • Dynamic hierarchisation of internal knowledge (shapes + associations) • determination of so-called contexts • various standpoints about a given notion • further desambiguation of queries April 2006 - Building the knowledge

  10. Knowledge Server a new way to design intelligent search applications • A Neuromimetic Engine • A new way to design intelligent search applications April 2006 - Building the knowledge

  11. Knowledge Servera new way to design intelligent search applications • Complete access to knowledge • through an extensive API (Application Programming Interface) • A Search Engine Application Builder Assistant • Understanding the power of knowledge • Testing search algorithms, e.g: • non-interactive direct access to results • interactive refinement of queries • Inventing new Search Experience April 2006 - Building the knowledge

  12. Demonstrationa complete tour of the Knowledge Server • Demonstrations April 2006 - Building the knowledge

  13. Demonstrationa complete tour of the Knowledge Server • Conditions • Application Builder Assistant • Never-ending Learning • Dynamic hierarchisation and contexts April 2006 - Building the knowledge

  14. Demonstrationconditions • Conditions • Application Builder Assistant • Never-ending Learning • Dynamic hierarchisation and contexts • Servo-database April 2006 - Building the knowledge

  15. Demonstrationconditions (1) • Warning • the examples shown in this presentation come from articles dated 1999 • 100,000 articles of the London Times • 290,000 shapes (words and phrases) • 5,000,000 associations (semantic links) April 2006 - Building the knowledge

  16. Demonstrationconditions (2) • Knowledge extraction (reading) • 1h on a laptop • Warning • Very raw indexation • no special pre-treatment of the articles • no filtering of • score tables and sport results • table of contents, indexes • headers and footers, etc. April 2006 - Building the knowledge

  17. Demonstrationapplication builder assistant • Conditions • Application Builder Assistant • Never-ending Learning • Dynamic hierarchisation and contexts • Servo-database April 2006 - Building the knowledge

  18. Demonstrationapplication builder assistant • Includes • Administration Monitoring • Work Session Place • Semantic Navigator April 2006 - Building the knowledge

  19. Demonstrationnever-ending learning • Conditions • Application Builder Assistant • Never-ending Learning • Dynamic hierarchisation and contexts • Servo-database April 2006 - Building the knowledge

  20. Demonstrationnever-ending learning (1) • Starts from scratch • no reference knowledge • no dictionnary, no thesaurus, etc. • Partial snapshot: "tony blair" • before less than 1,000articles • "tony blair" is linked to "prime" • "tony blair" is linked to "minister" • but the engine is unable to determine that "prime minister" makes sense as a phrase April 2006 - Building the knowledge

  21. Demonstrationnever-ending learning (2) • "tony blair" before 2,000 articles • the engine knew that "tony blair" was almost synonymous with • "prime" and "minister" • it understands now that "tony blair" and "Mr blair" are almost synonyms too April 2006 - Building the knowledge

  22. Demonstrationnever-ending learning (3) • "tony blair" before 3,000 articles • at this particular moment, the date of "november 26" is important; • but this can change because every piece of knowledge is constantly challenged • "prime minister" is now a shape by itself; • it becomes a strong attractor • a chunk of knowledge is transfered to it April 2006 - Building the knowledge

  23. Demonstrationnever-ending learning (4) • Acronyms are • not necessarily well-known • sometimes polysemous • Example of "WTO" • correctly resolved in "world trade organisation" April 2006 - Building the knowledge

  24. Demonstrationnever-ending learning (5) • Example of "bovine spongiform encephalopathy" • correct inverse resolution • correctly linked to important notions • case is managed ("bse"/"BSE") April 2006 - Building the knowledge

  25. Demonstrationnever-ending learning (6) • Knowledge can be very local to the documents processed • depends on subject and theme • includes editors names, etc. • Example of "General Augusto Pinochet" • number of articles written because of • implication of british court • recollection of past events (Falklands) • therefore pretty relevant knowledge extracted by the engine April 2006 - Building the knowledge

  26. Demonstrationnever-ending learning (7) • Knowledge about a subject is often spread out • around several key words • Example of "star wars" • current thing known = name of last episode • also knowledge about movie and business • link to "george lucas" April 2006 - Building the knowledge

  27. Demonstrationnever-ending learning (8) • Example of "george lucas" • additional knowledge • "steven spielberg" • "R2 D2" • "special effects" • Conclusion • complete knowledge about "star wars" • should also include knowledge about "george lucas" • and associated links April 2006 - Building the knowledge

  28. Demonstrationnever-ending learning (9) • Semantic Navigation • helps find out what to search • with "Pinochet" already: name of his spanish judge ("baltasar garzon") immediately displayed (help to recollection) • Example of "daimler" • additional links via "jürgen schrempp" April 2006 - Building the knowledge

  29. Demonstrationdynamic hierarchisation and contexts • Conditions • Application Builder Assistant • Never-ending Learning • Dynamic hierarchisation and contexts • Servo-database April 2006 - Building the knowledge

  30. Demonstrationdynamic hierarchisation and contexts (1) • Example of "social" • too much ambiguous for the engine • meaning has been dynamically split up into several sub-meanings • "social" yes • but "social" what? April 2006 - Building the knowledge

  31. Demonstrationdynamic hierarchisation and contexts (2) • Knowledge is a superset • of all what has been shown • Can sometimes represent • more than 500 words and phrases for a given concept • Example of "ethnic cleansing" • 34 bi-directional links • "KLA" (kosovo liberation army) • "british troops" • "military intervention" • "massacres" April 2006 - Building the knowledge

  32. Demonstrationdynamic hierarchisation and contexts (3) • "ethnic cleansing"(follows) • 813 other semantic links • "war crimes tribunal" • "rwanda"  • etc. • total: • 847 semantic links to retrieve articles • i.e. significant chance to find something interesting • even without the presence of "ethnic cleansing" in documents April 2006 - Building the knowledge

  33. Demonstrationdynamic hierarchisation and contexts (4) • 847 semantic links to be contextualised • "serbian ethnic cleansing" • steven spielberg" • "atrocities" • "civilians" • "post-mortem examination" • "genocide" • "humanitarian" • "kosovo" April 2006 - Building the knowledge

  34. Demonstrationdynamic hierarchisation and contexts (5) • 20 different contexts for "ethnic cleansing" • i.e. 20 different standpoints • First two contexts very different • 1st one centered around general presentation of Kosovo liberation • "ethnic albanians" (who live in Kosovo) • "KLA" • "serb"  • "bosnian" • "milosevic" April 2006 - Building the knowledge

  35. Demonstrationdynamic hierarchisation and contexts (6) • "ethnic cleansing" contexts (follows) • 2nd context about genocide in rwanda • "tutsi" • "rwanda"  • "hutu" • "pursue rwandan" • "killing tutsis" April 2006 - Building the knowledge

  36. Demonstrationdynamic hierarchisation and contexts (7) • Resultsdepend on quality of desambiguation by contexts • presentation of documents is correctly separated • avoid the need to read all the documents and do the sorting manually • Example of "kosovo" context • this document does not contain "ethnic cleansing" April 2006 - Building the knowledge

  37. Demonstrationdynamic hierarchisation and contexts (8) • Example of "rwanda" context • this document does not contain "ethnic cleansing" April 2006 - Building the knowledge

  38. That’s all folks

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