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Automating Class/Instance Representational Choices in Knowledge Bases

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Knowledge Engineering and Knowledge Management (EKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11313))

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Abstract

We present a method for making decisions as to whether an entity in a knowledge base should be a class or an instance based on external evidence in the form of corresponding textual corpora such as Wikipedia articles. The approach, based on machine classification of the text, avoids the need for feature engineering and provides valuable guidance when building or refining large knowledge bases. The approach works well over different domains and outperforms a variety of other state-of-the-art approaches.

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Notes

  1. 1.

    The code for our system and the data used will be made available before publication.

  2. 2.

    https://petscan.wmflabs.org/.

  3. 3.

    http://alt.qcri.org/semeval2016/task13/.

  4. 4.

    http://chem-eng.utoronto.ca/~datamining/dmc/zeror.htm.

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Author information

Authors and Affiliations

  1. University of Maryland, Baltimore County, Baltimore, USA

    Ankur Padia

  2. Nuance Communications, Sunnyvale, USA

    David Martin & Peter F. Patel-Schneider

Authors
  1. Ankur Padia
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  2. David Martin
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  3. Peter F. Patel-Schneider
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Corresponding author

Correspondence to Peter F. Patel-Schneider .

Editor information

Editors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Catherine Faron Zucker

  2. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  3. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Amedeo Napoli

  4. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Yannick Toussaint

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Padia, A., Martin, D., Patel-Schneider, P.F. (2018). Automating Class/Instance Representational Choices in Knowledge Bases. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-03667-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03666-9

  • Online ISBN: 978-3-030-03667-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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