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.

通过使用一种基于 Transformer 的推理模型 (ECEformer) 来学习事件的演化链，以及通过额外的时间预测任务来提高事件的及时性，本研究在六个基准数据集上验证了方法的先进性和有效性。