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Using active learning techniques for improving database schema matching methods

The schema matching problem consists of finding semantic correspondences between elements (e.g., attributes) of two database schemas. Typically, methods to solve this problem first use pair-wise functions called matchers to generate similarity scores values between pairs of elements from the two schemas. These scores are used as estimations of the correspondence between elements. Next, matchers are combined to establish which pairs of elements must be mapped when integrating the two schemas. For this, the best-known schema matching methods rely on fixed heuristics. We consider that using fixed heuristics is not always helpful to cope with a variety of database and element mismatch cases and argue that machine learning (ML) techniques comprise suitable alternatives to properly combine matchers. In this paper we propose ALMa (Active Learning Matching), a novel method for combining matchers based on active learning, which is an interesting and effective machine learning technique that efficiently exploits the users' expertise on the matching task. We report the results of experiments we carried out comparing ALMa with COMA, a well-known schema matching method in the literature based on fixed heuristics, and with YAM, a recent schema matching method based on supervised learning. In the experiments, ALMa achieved results better or at least similar to the baselines, while demanded less user effort, confirming the suitability of using active learning for combining matchers.