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.

近几十年来，知识图（KG）推理一直是一个有趣的话题。目前大多数研究都集中在预测不完整 KG 的缺失事实上。然而，由于实体之间随着时间的推移复杂的相互作用，用于预测未来事实的时态知识图谱（TKG）推理仍然面临着困境。本文提出了一种基于图卷积网络（GCN）的新颖复杂的时空依赖学习网络（STDN），以捕获不同时间戳下实体的底层相关性。具体来说，我们首先学习一个自适应邻接矩阵来描述实体的时间相邻事实的直接依赖关系，获得其先前的上下文嵌入。然后，提出了时空特征编码GCN（STE-GCN）来捕获实体的潜在时空依赖性，得到时空嵌入。最后，时间门单元用于整合先前的上下文嵌入和当前时间戳的时空嵌入，以更新实体进化嵌入以预测未来事实。 STDN 可以生成更具表现力的嵌入来捕获 TKG 中复杂的时空依赖性。在 WIKI、ICEWS14 和 ICEWS18 数据集上进行的大量实验证明，我们的 STDN 在时间推理任务方面比最先进的基线具有优势。