Multi-Hop Reasoning With Relation Based Node Quality Evaluation for Sparse Medical Knowledge Graph

Abstract

Medical knowledge graph (KG) is sparse KG that contains insufficient information and missing paths. Multi-hop reasoning is an effective approach of medical KG completion, since it offers logical insights of the underlying KG and shows more direct interpretability. However, existing methods based on reinforcement learning focus on the use of historical and current state information but ignore the importance of evaluating the quality of candidate nodes in sparse KGs. Especially, it is difficult for the agent to select the correct search actions in sparse KGs. Occasionally, the agent will be at a dilemma state (i.e., state trap), where few actions can be selected. To address the above issue, we propose an effective relation-based node quality evaluation (RNQE) model for multi-hop reasoning. This model has two merits: (1) it reduces the impact of insufficient information in sparse KGs by synthesizing the reasoning quality information (i.e., the potential reasoning contribution) of candidate nodes; (2) it avoids the state trap by encouraging the agents to explore the path along a set of nodes with more relations. Experiments on both benchmark and real-world medical knowledge graphs demonstrate the promising ability of our proposed method to improve the reasoning performance for KGs.