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An Engineering Approach for the Design of Hybrid Modelling Methods Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Matcher Composition Methods for Automatic Schema Matching

Authors : Daniel Nikovski, Alan Esenther, Xiang Ye, Mitsuteru Shiba, Shigenobu Takayama

Published in: Enterprise Information Systems

Publisher: Springer Berlin Heidelberg

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Abstract

We address the problem of automating the process of deciding whether two data schema elements match (that is, refer to the same actual object or concept), and propose several methods for combining evidence computed by multiple basic matchers. One class of methods uses Bayesian networks to account for the conditional dependency between the similarity values produced by individual matchers that use the same or similar information, so as to avoid overconfidence in match probability estimates and improve the accuracy of matching. Another class of methods relies on optimization switches that mitigate this dependency in a domain-independent manner. Experimental results under several testing protocols suggest that the matching accuracy of the Bayesian composite matchers can significantly exceed that of the individual component matchers, and the careful selection of optimization switches can improve matching accuracy even further.

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Metadata
Title
Matcher Composition Methods for Automatic Schema Matching
Authors
Daniel Nikovski
Alan Esenther
Xiang Ye
Mitsuteru Shiba
Shigenobu Takayama
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Enterprise Information Systems

Print ISBN: 978-3-642-40653-9

Electronic ISBN: 978-3-642-40654-6

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-40654-6_7

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