2008 | OriginalPaper | Buchkapitel
Boosting Schema Matchers
verfasst von : Anan Marie, Avigdor Gal
Erschienen in: On the Move to Meaningful Internet Systems: OTM 2008
Verlag: Springer Berlin Heidelberg
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Schema matching is recognized to be one of the basic operations required by the process of data and schema integration, and thus has a great impact on its outcome. We propose a new approach to combining matchers into ensembles, called Schema Matcher Boosting (
SMB
). This approach is based on a well-known machine learning technique, called boosting. We present a boosting algorithm for schema matching with a unique ensembler feature, namely the ability to choose the matchers that participate in an ensemble.
SMB
introduces a new promise for schema matcher designers. Instead of trying to design a perfect schema matcher that is accurate for all schema pairs, a designer can focus on finding better than random schema matchers. We provide a thorough comparative empirical results where we show that
SMB
outperforms, on average, any individual matcher. In our experiments we have compared
SMB
with more than 30 other matchers over a real world data of 230 schemata and several ensembling approaches, including the Meta-Learner of LSD. Our empirical analysis shows that
SMB
improves, on average, over the performance of individual matchers. Moreover,
SMB
is shown to be consistently dominant, far beyond any other individual matcher. Finally, we observe that
SMB
performs better than the Meta-Learner in terms of precision, recall and F-Measure.