Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual elements along the timeline. Although existing methods can learn good embeddings for each factual element in quadruples by integrating temporal information, they often fail to infer the evolution of temporal facts. This is mainly because of (1) insufficiently exploring the internal structure and semantic relationships within individual quadruples and (2) inadequately learning a unified representation of the contextual and temporal correlations among different quadruples. To overcome these limitations, we propose a novel Transformer-based reasoning model (dubbed ECEformer) for TKG to learn the Evolutionary Chain of Events (ECE). Specifically, we unfold the neighborhood subgraph of an entity node in chronological order, forming an evolutionary chain of events as the input for our model. Subsequently, we utilize a Transformer encoder to learn the embeddings of intra-quadruples for ECE. We then craft a mixed-context reasoning module based on the multi-layer perceptron (MLP) to learn the unified representations of inter-quadruples for ECE while accomplishing temporal knowledge reasoning. In addition, to enhance the timeliness of the events, we devise an additional time prediction task to complete effective temporal information within the learned unified representation. Extensive experiments on six benchmark datasets verify the state-of-the-art performance and the effectiveness of our method.

翻译：时间知识图谱推理常涉及沿时间线补全缺失的事实要素。尽管现有方法通过整合时间信息能够学习四元组中各事实要素的良好嵌入表示，但往往难以推断时间事实的演化过程。这主要源于：（1）未能充分探索单个四元组内部的结构特征与语义关联；（2）未能充分学习不同四元组之间上下文与时间关联的统一表征。为克服这些局限，我们提出一种面向时间知识图谱的新型Transformer推理模型（简称ECEformer），用于学习事件演化链（ECE）。具体而言，我们按时间顺序展开实体节点的邻接子图，构建事件演化链作为模型输入；随后利用Transformer编码器学习ECE中四元组内部的嵌入表示，并设计基于多层感知机（MLP）的混合上下文推理模块，在完成时间知识推理的同时学习ECE中四元组间的统一表征。此外，为增强事件的时效性，我们额外设计时间预测任务，在学习的统一表征中补全有效时间信息。六个基准数据集上的大量实验验证了该方法的最优性能与有效性。