  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.
