TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion

Abstract

Previous temporal knowledge graph (TKG) reasoning methods often focus exclusively on evolving representations. However, these methods suffer from the inadequacy of capturing the structural nuances of concurrent facts, the intricate relations in topological subgraphs, and the fusion of temporal information across timestamps. To address these challenges, this paper proposes a TKG reasoning method based on Topology-aware dynamic Relation graph and Temporal fusion (TaReT). First, TaReT proposes an innovative attention-based relational graph model, serving as a structural information encoder that captures the intricate structure of concurrent facts. Then, TaReT designs a topology-aware relational correlation unit to discern topological relation graphs of various patterns via an edge-level correlation network, yielding relation representations. Furthermore, TaReT introduces an inter-timestamp temporal information encoder which applies a dual-gate mechanism to integrate structural and relational information for temporal fusion. Finally, the temporal decoder is applied to output entity and relation predictions. Extensive experiments on four benchmark datasets establish TaReT’s superiority over leading TKG reasoning methods. On the ICEWS14 dataset, the MRR value of TaReT exceeds the reasoning baseline RE-GCN by 14.6%.