@inproceedings{zhang-etal-2020-mcmh,
title = "{MCMH}: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning",
author = "Zhang, Lu and
Yu, Mo and
Gao, Tian and
Yu, Yue",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.351/",
doi = "10.18653/v1/2020.findings-emnlp.351",
pages = "3948--3954",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
%A Zhang, Lu
%A Yu, Mo
%A Gao, Tian
%A Yu, Yue
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-mcmh
%X 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.
%R 10.18653/v1/2020.findings-emnlp.351
%U https://aclanthology.org/2020.findings-emnlp.351/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.351
%P 3948-3954
Markdown (Informal)
[MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning](https://aclanthology.org/2020.findings-emnlp.351/) (Zhang et al., Findings 2020)
ACL