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

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Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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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.

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Notes

  1. 1.

    http://metaquerier.cs.uiuc.edu/repository.

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Authors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Aola Yousfi, Moulay Hafid El Yazidi & Ahmed Zellou

Authors
  1. Aola Yousfi
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  2. Moulay Hafid El Yazidi
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  3. Ahmed Zellou
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Corresponding author

Correspondence to Aola Yousfi .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Thua Thien Hue Center of Information Technology, Hue, Vietnam

    Bao Hung Hoang

  3. Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

    Cong Phap Huynh

  4. Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

    Dosam Hwang

  5. Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

  6. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Yousfi, A., Yazidi, M.H.E., Zellou, A. (2020). Towards a Holistic Schema Matching Approach Designed for Large-Scale Schemas. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-63007-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63006-5

  • Online ISBN: 978-3-030-63007-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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