MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

Lu Zhang, Mo Yu, Tian Gao, Yue Yu


Abstract
Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
Anthology ID:
2020.findings-emnlp.351
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3948–3954
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.351
DOI:
10.18653/v1/2020.findings-emnlp.351
Bibkey:
Cite (ACL):
Lu Zhang, Mo Yu, Tian Gao, and Yue Yu. 2020. MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3948–3954, Online. Association for Computational Linguistics.
Cite (Informal):
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (Zhang et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.351.pdf
Optional supplementary material:
 2020.findings-emnlp.351.OptionalSupplementaryMaterial.pdf
Data
NELLNELL-995