Knowledge graph completion (KGC) aims to predict the missing links among
knowledge graph (KG) entities. Though various methods have been developed for
KGC, most of them can only deal with the KG entities seen in the training set
and cannot perform well in predicting links concerning novel entities in the
test set. Similar problem exists in temporal knowledge graphs (TKGs), and no
previous temporal knowledge graph completion (TKGC) method is developed for
modeling newly-emerged entities. Compared to KGs, TKGs require temporal
reasoning techniques for modeling, which naturally increases the difficulty in
dealing with novel, yet unseen entities. In this work, we focus on the
inductive learning of unseen entities' representations on TKGs. We propose a
few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the
missing entities from the links concerning unseen entities by employing a
meta-learning framework and utilizing the meta-information provided by only few
edges associated with each unseen entity. We construct three new datasets for
TKG few-shot OOG link prediction, and we propose a model that mines the
concept-aware information among entities. Experimental results show that our
model achieves superior performance on all three datasets and our concept-aware
modeling component demonstrates a strong effect.

该研究提出了一种用于预测时间知识图谱中未出现实体链接的新方法，其中利用元学习和新构建的数据集来实现对未见实体的归纳学习，在此基础上提出了概念感知模型来提高模型比较性能。