Query Relevance Feedback and Ontologies

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Query Relevance Feedback and Ontologies

• How to Make Queries Better

2
Overview

• Ranked Retrieval
• Relevance Feedback
• The Semantic Web and Ontologies

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Typical Web Retrieval Process
Link Following
Need
KeywordQuery
More Like this
4
Ranked Retrieval

• How can we present the best item to the user
  first

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What are we trying to do in IR

• Find the Document which is most similar to the
  query
• Ranking Interpretation
• show the best most similar document first
• then the next best most similar document
• and so on

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Bag of Words Model of Text

• Ignore the order of words in the document
• Just record whether a word appears in a document

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Similarity Measures

• Cosine Formula
• Measures how like a document is to a
  query/document
• See Kowalski Chapter 7

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Similarity as Ranking

• Use the Similarity Measure to rank the documents

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Relevance Feedback

• More Like this done properly

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Observation

• The user is probably in the best position to
  judge the relevance of a document
• Likewise the user is probably in the best
  position to judge which returned (highly ranked)
  documents are irrelevant

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Retrieval Process
No More Like This
Need
Analytic Query
More Like this
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Relevance Feedback in Nutshell

• Perform an initial retrieval
• Ask the user to indicate which documents are
  relevant/irrelevant
• Add all terms from relevant documents
• Remove all terms from irrelevant documents
• requery

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Variants

• Using Ranking and Weighting
• Pseudo relevance feedback
• use terms from all (highly ranked) retrieved
  documents
• Assumes highly ranked documents are a homogenous
  mass of relevant documents (Croft)
• very helpful if very few documents retrieved
• perpetuates errors/misunderstandings from
  original query

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Exercise

• What are advantages of positive feedback ?
• What are advantages of negative feedback ?
• Whis is best ?

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Relevance Feedback Conclusion

• Consistently proven an effective way to improve
  retrieval
• Biggest problem is getting users to engage in the
  interaction, especially if no highly relevant
  documents are in the initially retrieved set

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The Semantic Web

• Introduced by Tim Berners Lee and others in 2001
• http//www.sciam.com/article.cfm?articleID0004814
  4-10D2-1C70-84A9809EC588EF21
• Essentially about allowing computers and people
  to share the same world
• Central to the communication is the notion of an
  Ontology

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Ontologies
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Ontology Definition

• An ontology is an explicit specification of a
  conceptualization.
• A body of formally represented knowledge is
  based on a conceptualization the object,
  concepts and other entities that are presumed to
  exist in some area of interest and the
  relationships that hold them. A
  conceptualization is an abstract, simplified view
  of the world that we wish to represent for some
  purpose. Gruber, 1993

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Ontology example
Automobile
Car
Hot Hatch
Drop head coupe
Parts
Seat
Wheels
Engine
Sort of
Also Known as
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Improving Recall and/or Precision

• If you get too few documents
• Use more general terms in the query
• Use automobile instead of drop head coupe
• Use an alternative term which is more common
• Use car rather than automobile
• If you get too many (overall)
• Use a more specific term
• Use hot hatch rather than car

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Issues

• How are thesauri different from Ontologies
• Are we representing the world or words
• Is Wordnet an ontology ?
• Are Ontologies meant to be
• General
• Universal
• For a specific purpose ?

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Conclusions

• Ranked Retrieval
• similarity matching
• Relevance Feedback
• positive and negative feedback
• The Semantic Web and Ontologies