Hierarchical Knowledge-Enhancement Framework for multi-hop knowledge graph reasoning

Abstract

Multi-hop reasoning has gained significant attention in the area of knowledge graph completion, intending to predict missing facts through existing knowledge. However, due to sparsity knowledge and lack of reachable paths for the model to make sensible decisions, most multi-hop reasoning models suffer from degradation performance on the sparse knowledge graphs (KGs). Faced with this challenge, we propose the Hierarchical Knowledge-Enhancement Framework (Hi-KnowE), which improves reasoning performance over sparse KGs by conducting hierarchical dynamic path completion under various background knowledge guidance. Hi-KnowE is based on hierarchical reinforcement learning, breaking the task into high-level and low-level processes. Firstly, we apply high-level policy to reason relations and the relation action space is enlarged under rule guidance. Secondly, tail entities are obtained by low-level policy and semantic knowledge is used to refine the entity action space. The sparsity problem is alleviated by generating reliable information. Moreover, we introduce rule-based inner attention, adopting the overall semantics of relevant rules to assist agent reasoning. This strategy makes the path for the agent more logical and flexible. We further evaluate Hi-KnowE on four sparse datasets extracted from Wikidata and FreeBase. The results outperform the baseline models, demonstrating improvements of up to 1.5% and 1.9% in MRR and Hit@10.