Boosting Schema Matchers

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 MetaLearner in terms of precision, recall and F-Measure.