Measuring the Relative Performance of Schema Matchers

Schema matching is a complex process focusing on matching between concepts describing the data in heterogeneous data sources. There is a shift from manual schema matching, done by human experts, to automatic matching, using various heuristics (schema matchers). In this work, we consider the problem of linearly combining the results of a set of schema matchers. We propose the use of machine learning algorithms to learn the optimal weight assignments, given a set of schema matchers. We also suggest the use of genetic algorithms to improve the process efficiency.

• Type: Conference
• Year: 2005
• Where: WEBI
• Authors: Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal
• Keywords: Internet Technology, Manual Schema, Schema Matchers, Schema Matching, WEBI 2005