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

Lu Zhang, Mo Yu, Tian Gao, Yue Yu
2020 arXiv   pre-print
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
more » ... oring 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.
arXiv:2010.01735v1 fatcat:s2bbbdo37nc2pekfvip3afea6a