Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning

Abstract

Knowledge Graph (KG) reasoning has been an interesting topic in recent decades. Most current researches focus on predicting the missing facts for incomplete KG. Nevertheless, Temporal KG (TKG) reasoning, which is to forecast future facts, still faces with a dilemma due to the complex interactions between entities over time. This article proposes a novel intricate Spatiotemporal Dependency learning Network (STDN) based on Graph Convolutional Network (GCN) to capture the underlying correlations of an entity at different timestamps. Specifically, we first learn an adaptive adjacency matrix to depict the direct dependencies from the temporally adjacent facts of an entity, obtaining its previous context embedding. Then, a Spatiotemporal feature Encoding GCN (STE-GCN) is proposed to capture the latent spatiotemporal dependencies of the entity, getting the spatiotemporal embedding. Finally, a time gate unit is used to integrate the previous context embedding and the spatiotemporal embedding at the current timestamp to update the entity evolutional embedding for predicting future facts. STDN could generate the more expressive embeddings for capturing the intricate spatiotemporal dependencies in TKG. Extensive experiments on WIKI, ICEWS14, and ICEWS18 datasets prove our STDN has the advantage over state-of-the-art baselines for the temporal reasoning task.