UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference

Kewei Cheng, Ziqing Yang, Ming Zhang, Yizhou Sun


Abstract
Knowledge graph inference has been studied extensively due to its wide applications. It has been addressed by two lines of research, i.e., the more traditional logical rule reasoning and the more recent knowledge graph embedding (KGE). Several attempts have been made to combine KGE and logical rules for better knowledge graph inference. Unfortunately, they either simply treat logical rules as additional constraints into KGE loss or use probabilistic model to approximate the exact logical inference (i.e., MAX-SAT). Even worse, both approaches need to sample ground rules to tackle the scalability issue, as the total number of ground rules is intractable in practice, making them less effective in handling logical rules. In this paper, we propose a novel framework UniKER to address these challenges by restricting logical rules to be definite Horn rules, which can fully exploit the knowledge in logical rules and enable the mutual enhancement of logical rule-based reasoning and KGE in an extremely efficient way. Extensive experiments have demonstrated that our approach is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.
Anthology ID:
2021.emnlp-main.769
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9753–9771
Language:
URL:
https://aclanthology.org/2021.emnlp-main.769
DOI:
10.18653/v1/2021.emnlp-main.769
Bibkey:
Cite (ACL):
Kewei Cheng, Ziqing Yang, Ming Zhang, and Yizhou Sun. 2021. UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9753–9771, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference (Cheng et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.769.pdf
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