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.

翻译：静态知识图谱（KGs）的小样本关系学习近年来引起了广泛关注，而时序知识图谱（TKGs）的小样本学习研究则鲜有涉及。与KGs相比，TKGs包含丰富的时序信息，因此需要时序推理技术进行建模，这在时序背景下学习小样本关系带来了更大挑战。本文沿袭先前专注于静态KG小样本关系学习的研究范式，将两项基础性TKG推理任务（即内插与外推链接预测）扩展至一阶设定。我们提出四个新型大规模基准数据集，并开发了用于学习TKG中一阶关系的推理模型。实验结果表明，该模型在所有数据集的TKG链接预测任务上均能取得优异性能。