article

Matching large schemas: Approaches and evaluation

Authors:

Erhard RahmAuthors Info & Claims

Information Systems, Volume 32, Issue 6

Pages 857 - 885

https://doi.org/10.1016/j.is.2006.09.002

Published: 01 September 2007 Publication History

Abstract

Current schema matching approaches still have to improve for large and complex Schemas. The large search space increases the likelihood for false matches as well as execution times. Further difficulties for Schema matching are posed by the high expressive power and versatility of modern schema languages, in particular user-defined types and classes, component reuse capabilities, and support for distributed schemas and namespaces. To better assist the user in matching complex schemas, we have developed a new generic schema matching tool, COMA++, providing a library of individual matchers and a flexible infrastructure to combine the matchers and refine their results. Different match strategies can be applied including a new scalable approach to identify context-dependent correspondences between schemas with shared elements and a fragment-based match approach which decomposes a large match task into smaller tasks. We conducted a comprehensive evaluation of the match strategies using large e-Business standard schemas. Besides providing helpful insights for future match implementations, the evaluation demonstrated the practicability of our system for matching large schemas.

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Matching large schemas: Approaches and evaluation

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cover image Information Systems

Information Systems Volume 32, Issue 6

September, 2007

148 pages

ISSN:0306-4379

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Copyright © Elsevier B.V. © 2006.

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 September 2007

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https://dl.acm.org/doi/10.1177/0165551520952340
Athanasopoulos D(2022)Element Similarity Calculator in XML Schema MatchingProceedings of the 27th European Conference on Pattern Languages of Programs10.1145/3551902.3551970(1-10)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3551902.3551970
Xue XGuo J(2022)Word Embedding based Heterogeneous Entity Matching on Web of ThingsCompanion Proceedings of the Web Conference 202210.1145/3487553.3524704(941-947)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524704
Tekli J(2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-964:3(589-642)Online publication date: 11-Feb-2022
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Mountasser IOuhbi BHdioud FFrikh B(2021)Semantic-based Big Data integration framework using scalable distributed ontology matching strategyDistributed and Parallel Databases10.1007/s10619-021-07321-639:4(891-937)Online publication date: 1-Dec-2021
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Bernstein PMadhavan JRahm E(2020)Generic schema matching, ten years laterProceedings of the VLDB Endowment10.14778/3402707.34027104:11(695-701)Online publication date: 3-Jun-2020
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Ding GSun SWang G(2020)Schema matching based on SQL statementsDistributed and Parallel Databases10.1007/s10619-019-07268-938:1(193-226)Online publication date: 1-Mar-2020
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