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Joint relational triple extraction based on potential relation detection and conditional entity mapping

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Abstract

Joint relational triple extraction treats entity recognition and relation extraction as a joint task to extract relational triples, and this is a critical task in information extraction and knowledge graph construction. However, most existing joint models still fall short in terms of extracting overlapping triples. Moreover, these models ignore the trigger words of potential relations during the relation detection process. To address the two issues, a joint model based on Potential Relation Detection and Conditional Entity Mapping is proposed, named PRDCEM. Specifically, the proposed model consists of three components, i.e., potential relation detection, candidate entity tagging, and conditional entity mapping, corresponding to three subtasks. First, a non-autoregressive decoder that contains a cross-attention mechanism is applied to detect potential relations. In this way, different potential relations are associated with the corresponding trigger words in the given sentence, and the semantic representations of the trigger words are fully utilized to encode potential relations. Second, two distinct sequence taggers are employed to extract candidate subjects and objects. Third, an entity mapping module incorporating conditional layer normalization is designed to align the candidate subjects and objects. As such, each candidate subject and each potential relation are combined to form a condition that is incorporated into the sentence, which can effectively extract overlapping triples. Finally, the negative sampling strategy is employed in the entity mapping module to mitigate the error propagation from the previous two components. In a comparison with 15 baselines, the experimental results obtained on two widely used public datasets demonstrate that PRDCEM can effectively extract overlapping triples and achieve improved performance.

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Data availability

The datasets during the current study are available in the Github repository, https://github.com/hy-struggle/PRGC/tree/main/datahttps://github.com/hy-struggle/PRGC/tree/main/data.

Notes

  1. For example, the BertTokenizer will segmented “unwanted” to [“un”, “##want”, “##ed”]. Thus n words split into l tokens, where \(l \ge n\).

  2. https://github.com/hy-struggle/PRGC

  3. https://huggingface.co/bert-base-cased

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China (No.2020YFC2003502), the National Natural Science Foundation of China (No.62276038, No.62221005), the Foundation for Innovative Research Groups of Natural Science Foundation of Chongqing (No.cstc2019jcyjcxttX0002), and the Key Cooperation Project of Chongqing Municipal Education Commission (HZ2021008).

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Authors and Affiliations

  1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xiong Zhou & Man Gao

  2. Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xiong Zhou, Qinghua Zhang, Man Gao & Guoyin Wang

  3. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xiong Zhou, Qinghua Zhang, Man Gao & Guoyin Wang

  4. Key Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism, Chongqing, 400065, China

    Xiong Zhou, Qinghua Zhang, Man Gao & Guoyin Wang

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  1. Xiong Zhou
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  4. Guoyin Wang
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Contributions

All authors contributed to the study’s conception and design. Xiong Zhou contributed to the conception of the study and performed the experiments. Qinghua Zhang and Man Gao contributed to the manuscript preparation. Xiong Zhou and Man Gao performed the experiment analysis and wrote the manuscript. Qinghua Zhang and Guoyin Wang helped perform the analysis with constructive discussions.

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Correspondence to Qinghua Zhang.

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Appendix A: List of abbreviations

Appendix A: List of abbreviations

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Zhou, X., Zhang, Q., Gao, M. et al. Joint relational triple extraction based on potential relation detection and conditional entity mapping. Appl Intell 53, 29656–29676 (2023). https://doi.org/10.1007/s10489-023-05111-4

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