COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion

Jinpeng Li, Hang Yu, Xiangfeng Luo, Qian Liu


Abstract
Knowledge graph completion (KGC) aims to infer missing facts based on existing facts within a KG. Recently, research on generative models (GMs) has addressed the limitations of embedding methods in terms of generality and scalability. However, GM-based methods are sensitive to contextual facts on KG, so the contextual facts of poor quality can cause GMs to generate erroneous results. To improve the performance of GM-based methods for various KGC tasks, we propose a COntextual FactS GuIded GeneratioN (COSIGN) model. First, to enhance the inference ability of the generative model, we designed a contextual facts collector to achieve human-like retrieval behavior. Second, a contextual facts organizer is proposed to learn the organized capabilities of LLMs through knowledge distillation. Finally, the organized contextual facts as the input of the inference generator to generate missing facts. Experimental results demonstrate that COSIGN outperforms state-of-the-art baseline techniques in terms of performance.
Anthology ID:
2024.naacl-long.93
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1669–1682
Language:
URL:
https://aclanthology.org/2024.naacl-long.93
DOI:
Bibkey:
Cite (ACL):
Jinpeng Li, Hang Yu, Xiangfeng Luo, and Qian Liu. 2024. COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1669–1682, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (Li et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.93.pdf
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