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A Temporal Knowledge Graph (TKG) is a sequence of KGs with respectivetimestamps, 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 suchqueries as (\emph{query entity}, \emph{query relation}, \emph{?}, \emph{futuretimestamp}) about future. This is actually a matching task between a query andcandidate entities based on their historical structures, which reflectbehavioral trends of the entities at different timestamps. In addition, recentKGs provide background knowledge of all the entities, which is also helpful forthe matching. Thus, in this paper, we propose the \textbf{Hi}storical\textbf{S}tructure \textbf{Match}ing (\textbf{HiSMatch}) model. It applies twostructure encoders to capture the semantic information contained in thehistorical structures of the query and candidate entities. Besides, it adoptsanother encoder to integrate the background knowledge into the model. TKGreasoning experiments on six benchmark datasets demonstrate the significantimprovement of the proposed HiSMatch model, with up to 5.6\% performanceimprovement in MRR, compared to the state-of-the-art baselines.

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning