Corpus-based Schema Matching

Schema Matching is the problem of identifying corresponding elements in different schemas. Discovering these correspondences or matches is inherently difficult to automate. Past solutions have proposed a principled combination of multiple algorithms. However, these solutions sometimes perform rather poorly due to the lack of sufficient evidence in the schemas being matched. In this paper we show how a corpus of schemas and mappings can be used to augment the evidence about the schemas being matched, so they can be matched better. Such a corpus typically contains multiple schemas that model similar concepts and hence enables us to learn variations in the elements and their properties. We exploit such a corpus in two ways. First, we increase the evidence about each element being matched by including evidence from similar elements in the corpus. Second, we learn statistics about elements and their relationships and use them to infer constraints that we use to prune candidate mappings. We...

• Type: Conference
• Year: 2005
• Where: ICDE
• Authors: Jayant Madhavan, Philip A. Bernstein, AnHai Doan, Alon Y. Halevy
• Keywords: Candidate Mappings, Corpus-based Matching Outperforms, Database, ICDE 2005, Multiple Schemas