Query Relevance Feedback and Ontologies

1
Query Relevance Feedback and Ontologies

• How to Make Queries Better

2
Overview

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

3
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 ?
• Which 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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Ontologies
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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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Ontology Definition

• To standardize semantic terms, many areas use
  specific ontologies, which are hierarchical
  taxonomies of terms describing certain knowledge
  topics (Baeza-Yates Ribeiro-Neto, 1999, p143).
• Thesauri Ontologies for Information Retrieval.
• Entities, Relations.

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O 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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Thesauri

• Provide a map of a given field of knowledge
  concepts, relations.
• Provide a standard vocabulary for consistent
  indexing.
• Assist users with locating terms for proper query
  formulation.
• Ensure only one term from a synonym set is used
  for indexing and searching otherwise a searcher
  who uses one synonym and retrieves some useful
  documents may think the correct term has been
  used and the search has been exhaustive, without
  knowing that there are other useful documents
  under other synonyms.
• Provide classified hierarchies for broadening or
  narrowing a search if too many or too few
  documents are retrieved.
• Retrieval based on concepts rather than words
  (Baeza-Yates Ribeiro-Neto, 1999).

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WordNet Relations

• Examples are
• Synonyms e.g. couch / sofa / lounge
• Antonyms e.g. love / hate
• Hypernyms (broader) e.g. cat / tabby
• Hyponyms (narrower) e.g. cat / animal
• Meronym (part-of) e.g. finger / hand
• Meronym (made-of) e.g. snowflake / snow

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WordNet Demos

• See vancouver-webpages.com/wordnet
• See marimba.d.umn.edu/cgi-bin/similarity.cgi

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Conclusions

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