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Latent Relational Model for Relation Extraction

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The Semantic Web (ESWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11503))

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Abstract

Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore its use to address relation extraction tasks. We extend a relational model that has been shown to be effective in solving word analogies and adapt it to the relation extraction problem. Our experiments show that this approach outperforms the state-of-the-art methods on a relation extraction dataset, opening up a new research direction in discovering implicit relations in text through analogical reasoning.

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Notes

  1. 1.

    Based on the world described in the textual corpus.

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Acknowledgement

This work was conducted during an internship at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY, USA. We thank Anastas Stoyanovsky, Steven Pritko and Gabe Hart, software engineers at the IBM Watson Groups in Pittsburgh and Denver, USA, for helping and inspiring us during the “Fast Domain Adaptation in IBM Watson Discovery” project.

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Authors and Affiliations

  1. Department of Computer Science, University of Bari, Bari, Italy

    Gaetano Rossiello & Giovanni Semeraro

  2. IBM Research AI, Yorktown Heights, NY, USA

    Alfio Gliozzo & Nicolas Fauceglia

Authors
  1. Gaetano Rossiello
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  2. Alfio Gliozzo
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  3. Nicolas Fauceglia
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  4. Giovanni Semeraro
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Corresponding author

Correspondence to Gaetano Rossiello .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Pascal Hitzler

  2. KMi, The Open University, Milton Keynes, UK

    Miriam Fernández

  3. University of California, Santa Barbara, CA, USA

    Krzysztof Janowicz

  4. Maastricht University, Maastricht, The Netherlands

    Amrapali Zaveri

  5. Heriot-Watt University, Edinburgh, UK

    Alasdair J.G. Gray

  6. IBM Research, Dublin, Ireland

    Vanessa Lopez

  7. The Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Jönköping University, Jönköping, Sweden

    Karl Hammar

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Rossiello, G., Gliozzo, A., Fauceglia, N., Semeraro, G. (2019). Latent Relational Model for Relation Extraction. In: Hitzler, P., et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-21348-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21347-3

  • Online ISBN: 978-3-030-21348-0

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