Finding Hidden Correlations and Filtering out Incorrect Matchings with Compatibility Detection across Web Query Interfaces - PowerPoint PPT Presentation

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Finding Hidden Correlations and Filtering out Incorrect Matchings with Compatibility Detection across Web Query Interfaces

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Find out the hidden synonyms and build correlations to solve m:n ... Find Hidden Synonyms. Assume existence of hidden synonyms. Correlations between synonyms ... – PowerPoint PPT presentation

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Title: Finding Hidden Correlations and Filtering out Incorrect Matchings with Compatibility Detection across Web Query Interfaces


1
Finding Hidden Correlations and Filtering out
Incorrect Matchings with Compatibility Detection
across Web Query Interfaces
  • Lei Lei
  • June 11, 2004

2
Introduction
  • Deep Web scales rapidly
  • Proliferating sources with structured Info.
  • Vocabulary Converge to small size
  • Dynamic Queries instead of URLs

3
Complex Matching
  • Traditional methods focus on 11 matching
  • Query shemas form Complex Matchings
  • Mn

4
Web Query Interfaces
  • Web Query Interfaces

Attribute Group
5
Problems to solve
  • Relations are complicated and multi-ary
  • How to Judge the Relations of Synonyms?
  • How to pick out incorrect matchings?

6
Statement
  • Find out the hidden synonyms and build
    correlations to solve mn matching problem
  • Filter out false matchings and partially
    incorrect ones with the three step compatibility
    detection.

7
MGSsd and Improved Model
  • Original Hidden Model from MGSsd

8
Find Hidden Synonyms
  • Assume existence of hidden synonyms
  • Correlations between synonyms
  • Function HC(bi,bj)
  • Apply HC directly

9
Example
  • Synonyms on air booking domain
  • Set a Threshold

HC (b2,b4)
10
Compatibility Detection
  • Not all raw matching are correct
  • Clean partially correct or inaccurate ones
  • Three Steps
  • Transitivity Check
  • Examine Confidence
  • Subsumption

11
Compatibility Detection(Cont.)
  • Raw Matching Results
  • 1.Check Transitivity

3. Subsumption
2. Choose Confidence
12
Evaluation
  • Using Recall and Precision
  • Compare with MSGsd data
  • Perform Correlation and Compatibility on matching
    results from other researches

13
Contributions
  • mn mapping rather than only 11 mapping
  • Present a hidden synonym approach to
    statistically compute the correlation between
    synonym groups
  • Develop the Compatibility Detection approach to
    refine the raw mapping data
  • Suitable and efficient as the Web scales

14
Future Work
  • Figure out the HC Function
  • Minimum is feasible
  • Distinguish Trivial Difference in Confidence
  • Set up a proper threshold
  • Space Complexity
  • Type Subsumption
  • Departing datetime
  • Departing string

15
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