  Knowledge graph completion (KGC) can predict missing links and is crucial for
real-world knowledge graphs, which widely suffer from incompleteness. KGC
methods assume a knowledge graph is static, but that may lead to inaccurate
prediction results because many facts in the knowledge graphs change over time.
Recently, emerging methods have shown improved predictive results by further
incorporating the timestamps of facts; namely, temporal knowledge graph
completion (TKGC). With this temporal information, TKGC methods can learn the
dynamic evolution of the knowledge graph that KGC methods fail to capture. In
this paper, for the first time, we summarize the recent advances in TKGC
research. First, we detail the background of TKGC, including the problem
definition, benchmark datasets, and evaluation metrics. Then, we summarize
existing TKGC methods based on how timestamps of facts are used to capture the
temporal dynamics. Finally, we conclude the paper and present future research
directions of TKGC.
