Multi-timescale History Modeling for Temporal Knowledge Graph Completion

Temporal knowledge graph (TKG) has received great attention in recent years. However, the TKG is not always complete due to the missing of important facts, which has seriously hindered its wide application. Inferring missing facts in TKG is a critical and challenging task due to its highly dynamic nature. Most of the existing methods mainly focus on modeling the structural features and temporal dependencies of TKG to solve the temporal knowledge graph completion problem (TKGC). However, those methods only operate at a single timescale without considering the latent time variability of TKG and thus limit the performance of TKGC solutions. Therefore, we propose a novel method named MtGCN (Multi-timescale history modeling framework based on Graph Convolutional Networks) for completing TKG by self-adaptively modeling the multi-timescale history of the incomplete TKG. Firstly, MtGCN uses a structural encoder with a graph convolutional network to mine the latent semantic information and structural features of the TKG. Secondly, MtGCN uses GRU-based temporal encoder to learn the historical information at various timescales of the TKG. Finally, it generates effective entity and relation representations to infer the missing facts for the originally incomplete TKG. By conducting comprehensive experiments on 5 public datasets, the experimental results show that our proposed method MtGCN significantly outperforms the baselines by achieving the highest MRR and HITS@1,3,10.