Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network

Yubo Chen, Yunqi Zhang, Changran Hu, Yongfeng Huang


Abstract
Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.
Anthology ID:
2021.naacl-main.453
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5694–5703
Language:
URL:
https://aclanthology.org/2021.naacl-main.453
DOI:
10.18653/v1/2021.naacl-main.453
Bibkey:
Cite (ACL):
Yubo Chen, Yunqi Zhang, Changran Hu, and Yongfeng Huang. 2021. Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5694–5703, Online. Association for Computational Linguistics.
Cite (Informal):
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (Chen et al., NAACL 2021)
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https://aclanthology.org/2021.naacl-main.453.pdf
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