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Content Data Based Schema Matching

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Challenging Problems and Solutions in Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 634))

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Abstract

A novel automatic method for detecting corresponding attributes in schemas based on content data is studied. More specifically, our proposed method for the detection of coreferent attributes in schemas is based on a statistical and lexical comparison of content data and detected coreferent tuples across multiple datasets, which increase the possibility of correct schema matching. We will show that knowledge of even a small number of coreferent tuples is sufficient to establish correct matching between corresponding attributes of heterogeneous schemas. The behaviour of the novel schema matching technique has been evaluated on several real life datasets, giving a valuable insight in the influence of the different parameters of our approach on the results obtained.

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Notes

  1. 1.

    The order of datasets does not matter, i.e., there exists schema matching between corresponding attributes from the source dataset and the target dataset, and vice versa.

  2. 2.

    FreeDB, http://www.freedb.org/.

  3. 3.

    Discogs, http://www.discogs.com/data/.

  4. 4.

    http://hpi.de/naumann/projects/repeatability/datasets/cd-datasets.html.

  5. 5.

    Discogs, http://www.discogs.com/data/.

  6. 6.

    http://www.routeyou.com.

  7. 7.

    Google Places, http://developers.google.com/places/.

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Acknowledgments

This contribution is supported by the Foundation for Polish Science under International PhD Projects in Intelligent Computing. Project financed from The European Union within the Innovative Economy Operational Programme 2007–2013 and European Regional Development Fund. This work was also partially supported by the National Science Centre (contract no. UMO-2011/01/B/ST6/06908).

Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Marcin Szymczak & Sławomir Zadrożny

  2. Department of Telecommunications and Information Processing, University Ghent, St-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Marcin Szymczak, Antoon Bronselaer & Guy De Tré

Authors
  1. Marcin Szymczak
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  2. Antoon Bronselaer
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  3. Sławomir Zadrożny
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  4. Guy De Tré
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Corresponding author

Correspondence to Marcin Szymczak .

Editor information

Editors and Affiliations

  1. Dept. of Telecommunication & Inform proc, Ghent University, Gent, Belgium

    Guy de Trė

  2. Faculty of Maths and Information Science, Warsaw University of Technology, Warszawa, Poland

    Przemysław Grzegorzewski

  3. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Janusz Kacprzyk

  4. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Jan W. Owsiński

  5. Polish Academy of Sciences, Institute of Computer Science, Warszawa, Poland

    Wojciech Penczek

  6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Szymczak, M., Bronselaer, A., Zadrożny, S., De Tré, G. (2016). Content Data Based Schema Matching. In: Trė, G., Grzegorzewski, P., Kacprzyk, J., Owsiński, J., Penczek, W., Zadrożny, S. (eds) Challenging Problems and Solutions in Intelligent Systems. Studies in Computational Intelligence, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-30165-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-30165-5_14

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