Measuring the relative performance of schema matchers

Shlomo Berkovsky; Yaniv Eytani; Avigdor Gal

doi:10.1109/WI.2005.94

Measuring the relative performance of schema matchers

Shlomo Berkovsky, Yaniv Eytani, Avigdor Gal

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

7 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 language	English
Title of host publication	The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
Place of Publication	Los Alamitos
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	366-371
Number of pages	6
ISBN (Print)	076952415X
DOIs	https://doi.org/10.1109/WI.2005.94
Publication status	Published - 2005
Externally published	Yes
Event	2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005 - Compiegne Cedex, France Duration: 19 Sept 2005 → 22 Sept 2005

Other

Other	2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005
Country/Territory	France
City	Compiegne Cedex,
Period	19/09/05 → 22/09/05

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@inproceedings{3a71b1cbcfb3429dadca8eac7650371e,

title = "Measuring the relative performance of schema matchers",

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.",

author = "Shlomo Berkovsky and Yaniv Eytani and Avigdor Gal",

year = "2005",

doi = "10.1109/WI.2005.94",

language = "English",

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pages = "366--371",

booktitle = "The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005 ; Conference date: 19-09-2005 Through 22-09-2005",

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Berkovsky, S, Eytani, Y & Gal, A 2005, Measuring the relative performance of schema matchers. in The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos, pp. 366-371, 2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005, Compiegne Cedex, France, 19/09/05. https://doi.org/10.1109/WI.2005.94

Measuring the relative performance of schema matchers. / Berkovsky, Shlomo; Eytani, Yaniv; Gal, Avigdor.
The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). Los Alamitos: Institute of Electrical and Electronics Engineers (IEEE), 2005. p. 366-371.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Eytani, Yaniv

AU - Gal, Avigdor

PY - 2005

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N2 - 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.

AB - 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.

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DO - 10.1109/WI.2005.94

M3 - Conference proceeding contribution

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EP - 371

BT - The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Los Alamitos

T2 - 2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005

Y2 - 19 September 2005 through 22 September 2005

ER -

Measuring the relative performance of schema matchers

Abstract

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