HierarT: Multi-hop temporal knowledge graph forecasting with hierarchical reinforcement learning

Xuewei Luo, Anjie Zhu, Jiasheng Zhang, Jie Shao

Abstract: Highlights•Reasoning is dismantled into a relation level for relation reasoning and an entity level for entity reasoning.•Hybrid time encoding enhances the utilization of timestamp information in reasoning.•K-means-based reward shaping alleviates the issue of sparse reward matrices.•Text transformer is applied to deal with the limitation of a single information modality.