Schema Matching across Query Interfaces on the Deep Web

Schema matching is a crucial step in data integration. Many approaches to schema matching have been proposed so far. Different types of information about schemas, including structures, linguistic features and data types, etc have been used to match attributes between schemas. Relying on a single aspect of information about schemas for schema matching is not sufficient. Approaches have been proposed to combine multiple matchers taking into account different aspects of information about schemas. Weights are usually assigned to individual matchers so that their match results can be combined taking into account their different levels of importance. However, these weights have to be manually generated and are domain-dependent. We propose a new approach to combining multiple matchers using the Dempster-Shafer theory of evidence, which finds the top-k attribute correspondences of each source attribute from the target schema. We then make use of some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly effective.