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eTuner: tuning schema matching software using synthetic scenarios

Authors:

Mayssam Sayyadian,

Arnon S. RosenthalAuthors Info & Claims

The VLDB Journal — The International Journal on Very Large Data Bases, Volume 16, Issue 1

Pages 97 - 122

https://doi.org/10.1007/s00778-006-0024-z

Published: 25 January 2007 Publication History

Abstract

Most recent schema matching systems assemble multiple components, each employing a particular matching technique. The domain user mustthen tune the system: select the right component to be executed and correctly adjust their numerous “knobs” (e.g., thresholds, formula coefficients). Tuning is skill and time intensive, but (as we show) without it the matching accuracy is significantly inferior. We describe eTuner, an approach to automatically tune schema matching systems. Given a schema S, we match S against synthetic schemas, for which the ground truth mapping is known, and find a tuning that demonstrably improves the performance of matching S against real schemas. To efficiently search the huge space of tuning configurations, eTuner works sequentially, starting with tuning the lowest level components. To increase the applicability of eTuner, we develop methods to tune a broad range of matching components. While the tuning process is completely automatic, eTuner can also exploit user assistance (whenever available) to further improve the tuning quality. We employed eTuner to tune four recently developed matching systems on several real-world domains. The results show that eTuner produced tuned matching systems that achieve higher accuracy than using the systems with currently possible tuning methods.

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eTuner: tuning schema matching software using synthetic scenarios

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cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 16, Issue 1

January 2007

162 pages

ISSN:1066-8888

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 25 January 2007

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https://dl.acm.org/doi/10.14778/3476311.3476366
Martinez-Gil JYin SKüng JMorvan F(2021)Matching Large Biomedical Ontologies Using Symbolic RegressionThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487781(162-167)Online publication date: 29-Nov-2021
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