ExKGR: Explainable Multi-hop Reasoning for Evolving Knowledge Graph

Abstract

Knowledge graph reasoning is a popular approach to predict new facts in knowledge graphs (KGs) suffering from inherent incompleteness. Compared with the popular embedding-based approach, multi-hop reasoning approach is more interpretable. Multi-hop reasoning can be modeled as reinforcement learning (RL) in which the RL agent navigates in the KG. Despite high interpretability, the knowledge in real world evolves by the minute, previous approaches are based on static KG. To address the above challenges, we propose an explainable multi-hop reasoning approach (ExKGR) for practical scenario, aiming to reason the emerging entity in evolving KGs and provide evidentiary reasoning paths. Specifically, ExKGR can represent emerging entities by inductive learning of neighbors and the query. Furthermore, we restrict the RL action space of supernodes. Also, we use a dynamic reward instead of a binary reward in prior approaches. The experimental results on four benchmark datasets demonstrate that our approach significantly outperforms prior approaches.