  Few-shot relational learning for static knowledge graphs (KGs) has drawn
greater interest in recent years, while few-shot learning for temporal
knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain
rich temporal information, thus requiring temporal reasoning techniques for
modeling. This poses a greater challenge in learning few-shot relations in the
temporal context. In this paper, we follow the previous work that focuses on
few-shot relational learning on static KGs and extend two fundamental TKG
reasoning tasks, i.e., interpolated and extrapolated link prediction, to the
one-shot setting. We propose four new large-scale benchmark datasets and
develop a TKG reasoning model for learning one-shot relations in TKGs.
Experimental results show that our model can achieve superior performance on
all datasets in both TKG link prediction tasks.
