Temporal Knowledge Graph Embedding Learning through Multi-Aspect
Ruzhong Xie, Weihao Yu, Jin Huang · 2023
Temporal Knowledge Graph Completion (TKGC) aims at inferring the missing facts in Temporal Knowledge Ggraphs (TKGs), where facts are stored along with significant temporal information. However, lots of temporal knowledge graph models are extended from static knowledge graph methods, which usually consider message passing in TKGs either in predicate aspect or entity aspect. Leading to a result that entity embedding or predicate embedding obtained by these methods may lose a lot of information. To fill this gap, we propose a novel framework, MANet, to capture message passing between facts with the aspect of time, predicate and entity. We also note the irregular fact and introduce a residual component to study the irregularity of facts. Extensive experiments on five benchmarks demonstrate that our model outperforms the-state-of-art baselines, and the main indicators have improved obviously, among which MRR and H@1 have improved by four to eight percentage points on GDELT and WIKI datasets.