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Relation Repository Based Adaptive Clustering for Open Relation Extraction

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence (CCKS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1923))

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Abstract

Clustering-based relation discovery is one of the important methods in the field of open relation extraction (OpenRE). However, samples residing in semantically overlapping regions often remain indistinguishable. In this work, we propose an adaptive clustering method based on a relation repository to explicitly model the semantic differences between clusters to mitigate the relational semantic overlap in unlabeled data. Specifically, we construct difficult samples and use bidirectional margin loss to constrain the differences of each sample and apply self-supervised contrastive learning to labeled data. Combined with contrastive learning of unlabeled data, we construct a relation repository to explicitly model the semantic differences between clusters. Meanwhile, we place greater emphasis on the difficult samples located on the boundary, enabling the model to adaptively adjust the decision boundary, which lead to generate cluster-friendly relation representations to improve the effect of open relation extraction. Experiments on two public datasets show that our method can effectively improve the performance of open relation extraction.

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Acknowledgements

We thank the anonymous reviewers. This work is supported by the Open Project Program of the National Defense Key Laboratory of Electronic Information Equipment System Research under Grant DXZT-JCZZ-2017-009.

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Ke Chang & Ping Jian

Authors
  1. Ke Chang
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  2. Ping Jian
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Corresponding author

Correspondence to Ping Jian .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    Haofen Wang

  2. Chinese Academy of Sciences, Beijing, China

    Xianpei Han

  3. Harbin Institute of Technology, Harbin, China

    Ming Liu

  4. Nanjing University, Nanjing, China

    Gong Cheng

  5. University of South China, Hengyang, China

    Yongbin Liu

  6. Zhejiang University, Hangzhou, China

    Ningyu Zhang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chang, K., Jian, P. (2023). Relation Repository Based Adaptive Clustering for Open Relation Extraction. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_8

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  • DOI: https://doi.org/10.1007/978-981-99-7224-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7223-4

  • Online ISBN: 978-981-99-7224-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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