Learning from Reconstruction: A Two-Stage Global-to-Local Framework for Temporal Knowledge Graph Completion

In recent years, Temporal Knowledge Graphs (TKGs) have developed rapidly and Temporal Knowledge Graph Completion (TKGC) aims at completing the missing parts of incomplete TKGs. Many methods have been proposed for TKGC, but there are still unresolved issues. Current methods have not fully explored the temporal distribution features of different entities, and the modeling approaches for historical graphs overlook internal temporal correlations. To solve these issues, we propose LeREC, Learning from REConstruction: A Two-Stage Global-to-Local Framework for TKGC. In the first stage, we use the temporal distribution features of entities as input and employ VGAE to reconstruct the simplified global historical graph. In the second stage, we introduce time-aware relational attributes to integrate multiple local historical graphs and predict missing entities in the query. Experiments conducted on benchmark datasets demonstrate the superiority of our model.