Measuring the relative performance of schema matchers

Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationThe 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
Place of PublicationLos Alamitos
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages366-371
Number of pages6
ISBN (Print)076952415X
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005 - Compiegne Cedex, France
Duration: 19 Sep 200522 Sep 2005

Other

Other2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005
CountryFrance
CityCompiegne Cedex,
Period19/09/0522/09/05

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