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ESWC 2019: The Semantic Web pp 36–51Cite as

A Hybrid Graph Model for Distant Supervision Relation Extraction

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

Abstract

Distant supervision has advantages of generating training data automatically for relation extraction by aligning triples in Knowledge Graphs with large-scale corpora. Some recent methods attempt to incorporate extra information to enhance the performance of relation extraction. However, there still exist two major limitations. Firstly, these methods are tailored for a specific type of information which is not enough to cover most of the cases. Secondly, the introduced extra information may contain noise. To address these issues, we propose a novel hybrid graph model, which can incorporate heterogeneous background information in a unified framework, such as entity types and human-constructed triples. These various kinds of knowledge can be integrated efficiently even with several missing cases. In addition, we further employ an attention mechanism to identify the most confident information which can alleviate the side effect of noise. Experimental results demonstrate that our model outperforms the state-of-the-art methods significantly in various evaluation metrics.

Keywords

  • Distant supervision
  • Relation extraction
  • Heterogeneous information
  • Hybrid graph

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Notes

  1. 1.

    https://github.com/Apeoud/HG-DSRE.git.

  2. 2.

    www.wikidata.org/.

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Acknowledgement

This work was supported by National Natural Science Foundation of China Key (U1736204) and National Key R&D Program of China (2018YFC0830200).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Shangfu Duan, Huan Gao, Bing Liu & Guilin Qi

Authors
  1. Shangfu Duan
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  2. Huan Gao
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  3. Bing Liu
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  4. Guilin Qi
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Corresponding author

Correspondence to Guilin Qi .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Prof. Dr. 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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Duan, S., Gao, H., Liu, B., Qi, G. (2019). A Hybrid Graph Model for Distant Supervision Relation Extraction. In: , 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_3

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  • DOI: https://doi.org/10.1007/978-3-030-21348-0_3

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