A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\emph{subject}, \emph{relation}, \emph{object}, \emph{timestamp}) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (\emph{query entity}, \emph{query relation}, \emph{?}, \emph{future timestamp}) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the \textbf{Hi}storical \textbf{S}tructure \textbf{Match}ing (\textbf{HiSMatch}) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6\% performance improvement in MRR, compared to the state-of-the-art baselines.

本文提出了HiSMatch模型，该模型应用了两个结构编码器来捕获查询和候选实体的历史结构的语义信息，并采用另一个编码器将背景知识集成到模型中，经过在六个基准数据集上的TKG推理实验，HiSMatch模型的性能较现有的基线模型提高了5.6％的MRR。

HiSMatch: 基于历史结构匹配的时间知识图谱推理