Graph Self-attention Mechanism for Interpretable Multi-hop Knowledge Graph Link Prediction

Knowledge Graphs (KGs) are extensively used in recommendation systems and information retrieval but often suffer from incompleteness. A popular solution to this problem is multi-hop inference through a reinforcement learning framework, which provides an interpretable path for predicting missing links in KGs. Most previous work focuses on improving the performance of multi-hop link prediction. However, it has been observed that many multi-hop paths generated by these methods are irrational; they often fail to reasonably explain the predicted answer entities. To address this challenge, we introduce the Joint Multi-hop Link Prediction (JMLP) framework. The framework consists of a relation attention network and an entity attention network, which collaboratively generate the reasoning paths. The relation attention module utilizes an induction network to encode historical paths and employs the graph self-attention mechanism to refine the interaction of relation contextual information. The entity attention module uses the graph attention mechanism to obtain the aggregated contextual features and leverages self-attention to strengthen the correlation between local and global contextual entity features. Extensive experiments on five datasets validate the effectiveness of our approach, demonstrating significant improvements both in predictive performance and interpretability compared to state-of-the-art methods.