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Multi-hop reasoning approaches over knowledge graphs infer a missingrelationship between entities with a multi-hop rule, which corresponds to achain of relationships. We extend existing works to consider a generalized formof multi-hop rules, where each rule is a set of relation chains. To learn suchgeneralized rules efficiently, we propose a two-step approach that firstselects a small set of relation chains as a rule and then evaluates theconfidence of the target relationship by jointly scoring the selected chains. Agame-theoretical framework is proposed to this end to simultaneously optimizethe rule selection and prediction steps. Empirical results show that ourmulti-chain multi-hop (MCMH) rules result in superior results compared to thestandard single-chain approaches, justifying both our formulation ofgeneralized rules and the effectiveness of the proposed learning framework.

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