2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05) Measuring the Relative Performance of Schema Matchers Compi?gne University of Technology, France September 19-September 22 ISBN: 0-7695-2415-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2005.94
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.
Citation:
Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal, "Measuring the Relative Performance of Schema Matchers," wi, pp.366-371, 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||