semantic search from truevert
DESCRIPTION
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.TRANSCRIPT
Semantic Search
From the Truevert perspective
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Harness meaning for better search results
Approaches to semantic searchReasoning
engines
Natural language
understanding
Ontology search/ expansion
Contextual analysis
Address somewhat different problems
Semantic Web?
People are inconsistentPeople are lazyPeople are creativePeople are innovativePeople liePeople are competitive
Language is dynamic
Jabberwocky Effect
Humpty Dumpty Syndrome
Making up new words Using old words in new ways
blog Twitter
Strike
Bank
Words are ambiguous
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
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
30%
13%
9%6%
6%
5%
4%
4%
3%
3%
17%
Where can I findWhere can I find encyclopedic resources onWhere can I learn aboutWhat isWhere can I seeWhere can I find information aboutHow can IWhere can I find information onWhere can I find the Web site forWhere can I readOther
Do people ask interesting questions?
No ontology, taxonomy, or thesaurus needed
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
Japanese women’s blogs (the context)
化粧Makeup
Semantic Search Example
Search for:
化粧Makeup
肌Skin
美Beauty
美容Beauty of figure or
form
ケアCare
dhcBrand 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
Context for 化粧
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 searchedTerms are weighted by language model
Expand query to give context
Truevert Green
See the world from a green perspective
Custom Search
Custom Green Search
Custom Health Search
The Vertical Semantic Search Engine
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