  In the last few years, the solution to Knowledge Graph (KG) completion via
learning embeddings of entities and relations has attracted a surge of
interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by
associating static triples with timestamps forming quadruples. Different from
KGs and TKGs in the transductive setting, constantly emerging entities and
relations in incomplete TKGs create demand to predict missing facts with unseen
components, which is the extrapolation setting. Traditional temporal knowledge
graph embedding (TKGE) methods are limited in the extrapolation setting since
they are trained within a fixed set of components. In this paper, we propose a
Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which
is trained on link prediction tasks sampled from the existing TKGs and tested
in the emerging TKGs with unseen entities and relations. Specifically, we
meta-train a GNN framework that captures relative position patterns and
temporal sequence patterns between relations. The learned embeddings of
patterns can be transferred to embed unseen components. Experimental results on
two different TKG extrapolation datasets show that MTKGE consistently
outperforms both the existing state-of-the-art models for knowledge graph
extrapolation and specifically adapted KGE and TKGE baselines.
