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Machine Learning Models: Combining Evidence of Similarity for XML Schema Matching

  • Tran Hong-Minh
  • Dan Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3915)

Abstract

Matching schemas at an element level or structural level is generally categorized as either hybrid, which uses one algorithm, or composite, which combines evidence from several different matching algorithms for the final similarity measure. We present an approach for combining element-level evidence of similarity for matching XML schemas with a composite approach. By combining high recall algorithms in a composite system we reduce the number of real matches missed. By performing experiments on a number of machine learning models for combination of evidence in a composite approach and choosing the SMO for the high precision and recall, we increase the reliability of the final matching results. The precision is therefore enhanced (e.g., with data sets used by Cupid and suggested by the author of LSD, our precision is respectively 13.05% and 31.55% higher than COMA and Cupid on average).

Keywords

Learning Category Schema Match Machine Learning Model Composite Approach Approximate String Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tran Hong-Minh
    • 1
  • Dan Smith
    • 1
  1. 1.School of Computing SciencesUniversity of Of East AngliaNorwichUK

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