Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks.