Homogeneous Entity Context Enhanced Representation Network for Temporal Knowledge Graph Reasoning

Aiming to extrapolate missing facts in the future timestamp, temporal knowledge graph (TKG) reasoning has significant practical value across various applications. Most existing methods mainly rely on direct historical interactions to extract the temporal features of entities and have achieved promising performance in some specific scenes. However, these methods are constrained when it comes to predicting emerging events that lack such direct historical interaction information. To address this limitation, we propose a novel approach called the Homogeneous Entity Context Enhanced Representation Network (HECERN). Homogeneous entities are defined as entities with similar behavior patterns. HECERN takes advantage of the complex interaction connections among the relations of homogeneous entities, which contain rich and relevant context information for accurate prediction of emerging events. Specifically, we generate corresponding entity context subgraph for each history subgraph and extract dependencies for homogeneous entities and neighboring entities based on these two subgraphs, respectively. In addition, we introduce an attention-guided fusion mechanism that dynamically integrates information from neighbor-level dependencies and homogeneous-level dependencies to effectively generate the final entity representation. The effectiveness of HECERN is demonstrated through comprehensive experiments on three publicly available event-based TKG datasets. The results clearly indicate that our proposed approach significantly outperforms state-of-the-art methods in accurately predicting future events. (The source code is available at https://anonymous.4open.science/r/HECERN-2C6B.)