CMC: Combining Multiple Schema-Matching Strategies Based on Credibility Prediction

Schema matching, which tries to find semantic correspondences between schema elements, is a key operation in data engineering. Combining multiple matching strategies is a very promising technique for schema matching. To overcome the limitations of existing combination systems and to achieve better performances, in this paper the CMC system is proposed, which combines multiple matchers based on credibility prediction. We first predict the accuracy of each matcher on the current matching task, and accordingly calculate each matcher’s credibility. These credibilities are then used as weights in aggregating the matching results of different matchers into a combined one. The credibility prediction procedure is introduced at length, and two strategies (i.e. manual rule and machine learning) are presented. Our experiments on real world schemas validate the merits of our system.