Multi-hop Knowledge Reasoning with Deep Reinforcement Learning

Multi-hop reasoning is an effective and explainable approach to predicting missing facts in Knowledge Graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and searches over the KG to find an evidential path. However, there are few RL based reasoning methods in Knowledge Forests (KFs), the existing approach is usually to transfer multi-hop reasoning methods in the KG to the KF. However, this way often leads to a long reasoning path with a lot of redundant information. And the existing reasoning methods tend to capture vector representations from local structures, which will lead to get meaningless paths. In order to solve these two problems, we present, in this paper, a deep reinforcement learning based model named by MultiKR for KFs and KGs, which reduces redundant information in the path by learning global vector representations. In addition, we construct a novel reward function that guides the agent's choice of actions by considering accuracy, diversity and rationality to get valid and short path. We test our model on two KF datasets, that is, COMPUTER SCIENCE (CS) and MEDICINE (MED), and two KG datasets FB15K-237 and NELL-995. Extensive experiments show that our model is effective and competitive with many current state-of-the-art methods, and also performs well in practice.