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Semantic Search From Truevert
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Semantic Search From Truevert


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This is a version of a presentation prepared for the Second AltSearch Engines Conference. San Francisco, March 30, 2009. These slides are a bit different from what was presented. …

This is a version of a presentation prepared for the Second AltSearch Engines Conference. San Francisco, March 30, 2009. These slides are a bit different from what was presented.

There are several approaches to semantic search. Their unifying theme is an attempt to use meaning to improve the quality of information that people receive.

Human language presents several challenges to using meaning. Among these are ambiguity, synonymy, and creativity.

Natural concepts do not fit well within an ontology because how people think of things depends on their context and their needs.

Truevert learns the meaning of words from their use in the language. It works in any language.

Custom search engines can be easily constructed that represent a specific point of view.

Published in: Technology, Health & Medicine

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  • Transcript

    • 1. Semantic Search From the True vert perspective Powered by
    • 2. Harness meaning for better search results
    • 3. Approaches to semantic search Address somewhat different problems Reasoning engines Natural language understanding Ontology search/ expansion Contextual analysis
    • 4. Semantic Web? People are inconsistent People are lazy People are creative People are innovative People lie People are competitive
    • 5. Language is dynamic Jabberwocky Effect Humpty Dumpty Syndrome Making up new words Using old words in new ways blog Twitter
    • 6. Strike Bank Words are ambiguous
    • 7. How ambiguous? Look it up! The companies have agreed to a brief delay in implementing their agreement. 37 14 39 17 54 62 20 8 84 8 7 9 7,788,584,618,680,320 possible interpretations Each word disambiguates the others # definitions
    • 8. A dog A pet Man’s best friend Rescuer Resident of Colorado An animal Service animal How many ways can you categorize Chester? Tan things A living thing Things with hair Tail wagger Mutt Donny’s dog Mammal
    • 9. Do people ask interesting questions?
    • 10. No ontology, taxonomy, or thesaurus needed
    • 11. Blah blah blah court blah blah blah lawyer blah blah blah blah bailiff blah blah blah blah blah. Blah blah court blah blah blah basketball blah blah blah blah blah blah freethrow blah blah blah blah. Model word use patterns from documents Legal context Sport context
    • 12. Japanese women’s blogs (the context)
    • 13. Semantic Search Example Search for: 化粧 Makeup
    • 14. Context for 化粧 化粧 Makeup 肌 Skin 美 Beauty 美容 Beauty of figure or form ケア Care dhc Brand of skin care products スキン Skin キレイ Pretty 始め Origin 口コミ Word of mouth 通販 Mail order
    • 15. Expand query to give context 化粧 化粧 , 美容 , 肌 , 美 , ケア , dhc , スキン , キレイ , 始め , 口コミ , 通販 Query as entered Query as searched Terms are weighted by language model
    • 16. Truevert Green See the world from a green perspective
    • 17.  
    • 18.  
    • 19.  
    • 20. Custom Search
    • 21. Custom Green Search
    • 22. Custom Health Search
    • 23. The Vertical Semantic Search Engine Powered by Contact: [email_address] 805-918-4612