Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas

TitreTowards a Holistic Schema Matching Approach Designed for Large-Scale Schemas
Publication TypeJournal Article
Year of Publication2020
AuthorsYousfi, A, Yazidi, MHE, Zellou, A
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12496 LNAI
Pagination3-15
Mots-clésArtificial intelligence, Data integration, High quality, High-accuracy, Holistic schema matching, Real world domain, Search spaces, Semantic correspondence, Semantic similarity measures, Semantics
Abstract

Holistic schema matching is a fundamental challenge in the big data integration domain. Ideally, clusters of semantically corresponding elements are created and are updated as more schemas are matched. Developing a high-quality holistic schema matching approach is critical for two main reasons. First, identifying as many accurate and holistic semantic correspondences as possible right from the beginning. Second, reducing considerably the search space. Nevertheless, this problem is challenging since overlapping schema elements are not available. Identifying schema overlaps is further complicated for two main reasons: (1) there is a large number of schemas; and (2) overlaps vary for different schemas. In this paper we present HMO, a Holistic schema Matching approach based on schema Overlaps and designed for large-scale schemas. HMO can balance the search space and the quality of the holistic semantic correspondences. To narrow down the search space, HMO matches schemas based on their overlaps. To obtain high-accuracy, HMO uses an existing high-quality semantic similarity measure. Experimental results on four real-world domains show effectiveness and scalability of our matching approach. © 2020, Springer Nature Switzerland AG.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097530171&doi=10.1007%2f978-3-030-63007-2_1&partnerID=40&md5=aed75e2b584acebebdce7bf0dc2cc076
DOI10.1007/978-3-030-63007-2_1
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