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Enhancing Document-Level Relation Extraction by Entity Knowledge Injection

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The Semantic Web – ISWC 2022 (ISWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13489))

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Abstract

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.

National Natural Science Foundation of China (No. 61872172).

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Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xinyi Wang, Zitao Wang & Wei Hu

  2. National Institute of Healthcare Data Science, Nanjing University, Nanjing, China

    Wei Hu

  3. Huawei Technologies Co., Ltd., Shanghai, China

    Weijian Sun

Authors
  1. Xinyi Wang
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  2. Zitao Wang
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  3. Weijian Sun
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  4. Wei Hu
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Corresponding author

Correspondence to Wei Hu .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Ulrike Sattler

  2. University of Chile, Santiago, Chile

    Aidan Hogan

  3. University of Cape Town, Cape Town, South Africa

    Maria Keet

  4. University of Bologna, Bologna, Italy

    Valentina Presutti

  5. Universidade Federal do Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  6. National Institute of Informatics, Tokyo, Japan

    Hideaki Takeda

  7. Orange, Belfort, France

    Pierre Monnin

  8. Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

  9. University of Bari, Bari, Italy

    Claudia d’Amato

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Wang, X., Wang, Z., Sun, W., Hu, W. (2022). Enhancing Document-Level Relation Extraction by Entity Knowledge Injection. In: Sattler, U., et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-19433-7_3

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  • Online ISBN: 978-3-031-19433-7

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