MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
This work addresses knowledge graph reasoning for AI applications, representing an incremental improvement over existing methods.
The paper tackles the problem of multi-hop reasoning over knowledge graphs by extending single-chain rules to multi-chain multi-hop rules, achieving superior results compared to standard approaches.
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.