Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

Abstract

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop 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 human handle 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.

Publication
International Joint Conference on Artificial Intelligence, IJCAI-20
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Guojia Wan
PhD Student (2018-) @ Wuhan U.

My research interests include Knowledge Graph representation/reasoning, Reinforcement Learning in Graph Data, Graph Neural Networks.