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PROCLAIM: An Unsupervised Approach to Discover Domain-Specific Attribute Matchings from Heterogeneous Sources

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Advanced Information Systems Engineering (CAiSE 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 386))

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Abstract

Schema matching is a critical problem in many applications where the main goal is to match attributes coming from heterogeneous sources. In this paper, we propose PROCLAIM (PROfile-based Cluster-Labeling for AttrIbute Matching), an automatic, unsupervised clustering-based approach to match attributes of a large number of heterogeneous sources. We define the concept of attribute profile to characterize the main properties of an attribute using: (i) the statistical distribution and the dimension of the attribute’s values, (ii) the name and textual descriptions related to the attribute. The attribute matchings produced by PROCLAIM give the best representation of heterogeneous sources thanks to the cluster-labeling function we defined. We evaluate PROCLAIM on 45,000 different data sources coming from oil and gas authority open data website (The data is published under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)). The results we obtain are promising and validate our approach.

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Notes

  1. 1.

    Data from: https://www.kaggle.com/.

  2. 2.

    The data is published under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

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Author information

Authors and Affiliations

  1. Services Pétroliers Schlumberger, 34000, Montpellier, France

    Molood Arman & Sylvain Wlodarczyk

  2. Université Paris-Saclay, CNRS, Laboratoire de Recherche en Informatique, 91405, Orsay, France

    Molood Arman, Nacéra Bennacer Seghouani & Francesca Bugiotti

Authors
  1. Molood Arman
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  2. Sylvain Wlodarczyk
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  3. Nacéra Bennacer Seghouani
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  4. Francesca Bugiotti
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Corresponding author

Correspondence to Molood Arman .

Editor information

Editors and Affiliations

  1. Université Paris1 Panthéon-Sorbonne, Paris, France

    Nicolas Herbaut

  2. University of Melbourne, Melbourne, VIC, Australia

    Marcello La Rosa

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Arman, M., Wlodarczyk, S., Bennacer Seghouani, N., Bugiotti, F. (2020). PROCLAIM: An Unsupervised Approach to Discover Domain-Specific Attribute Matchings from Heterogeneous Sources. In: Herbaut, N., La Rosa, M. (eds) Advanced Information Systems Engineering. CAiSE 2020. Lecture Notes in Business Information Processing, vol 386. Springer, Cham. https://doi.org/10.1007/978-3-030-58135-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-58135-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58134-3

  • Online ISBN: 978-3-030-58135-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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