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Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

We investigate a machine learning approach to ontology instance matching. We apply syntactic and lexical text analysis as well as tensor-based data representation as means for feature engineering effectively supporting supervised learning based on logistic regression. We experimentally evaluate our approach in the scenario of the SABINE Data linking subtask defined by Ontology Alignment Evaluation Initiative. We show that, as far as the prediction of non-trivial matches is concerned, the use of the proposed tensor-based modelling of lexical and syntactical properties of the ontology instances enables achieving a significant quality improvement.

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Acknowledgments

This work is supported by the Polish National Science Centre, grant DEC-2011/01/D/ST6/06788.

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Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznan University of Technology, ul. Piotrowo 3a, 60-965, Poznan, Poland

    Andrzej Szwabe, Paweł Misiorek, Jarosław Bąk & Michał Ciesielczyk

Authors
  1. Andrzej Szwabe
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  2. Paweł Misiorek
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  3. Jarosław Bąk
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  4. Michał Ciesielczyk
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Corresponding author

Correspondence to Andrzej Szwabe .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Szwabe, A., Misiorek, P., Bąk, J., Ciesielczyk, M. (2017). Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_55

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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