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Towards Nonmonotonic Relational Learning from Knowledge Graphs

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Inductive Logic Programming (ILP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10326))

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Abstract

Recent advances in information extraction have led to the so-called knowledge graphs (KGs), i.e., huge collections of relational factual knowledge. Since KGs are automatically constructed, they are inherently incomplete, thus naturally treated under the Open World Assumption (OWA). Rule mining techniques have been exploited to support the crucial task of KG completion. However, these techniques can mine Horn rules, which are insufficiently expressive to capture exceptions, and might thus make incorrect predictions on missing links. Recently, a rule-based method for filling in this gap was proposed which, however, applies to a flattened representation of a KG with only unary facts. In this work we make the first steps towards extending this approach to KGs in their original relational form, and provide preliminary evaluation results on real-world KGs, which demonstrate the effectiveness of our method.

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Notes

  1. 1.

    http://imdb.com.

  2. 2.

    http://people.mpi-inf.mpg.de/~gadelrab/downloads/ILP2016.

  3. 3.

    https://github.com/htran010589/nonmonotonic-rule-mining.

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Acknowledgements

We thank anonymous reviewers for their insightful suggestions and Jacopo Urbani for his helpful comments on an earlier version of this paper.

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Authors and Affiliations

  1. Max-Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany

    Hai Dang Tran, Daria Stepanova, Mohamed H. Gad-Elrab & Gerhard Weikum

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Francesca A. Lisi

Authors
  1. Hai Dang Tran
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  2. Daria Stepanova
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  3. Mohamed H. Gad-Elrab
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  4. Francesca A. Lisi
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  5. Gerhard Weikum
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Corresponding author

Correspondence to Daria Stepanova .

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Editors and Affiliations

  1. Department of Computer Science, University of York, York, United Kingdom

    James Cussens

  2. Imperial College London, London, United Kingdom

    Alessandra Russo

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Tran, H.D., Stepanova, D., Gad-Elrab, M.H., Lisi, F.A., Weikum, G. (2017). Towards Nonmonotonic Relational Learning from Knowledge Graphs. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-63342-8_8

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