  We study the knowledge extrapolation problem to embed new components (i.e.,
entities and relations) that come with emerging knowledge graphs (KGs) in the
federated setting. In this problem, a model trained on an existing KG needs to
embed an emerging KG with unseen entities and relations. To solve this problem,
we introduce the meta-learning setting, where a set of tasks are sampled on the
existing KG to mimic the link prediction task on the emerging KG. Based on
sampled tasks, we meta-train a graph neural network framework that can
construct features for unseen components based on structural information and
output embeddings for them. Experimental results show that our proposed method
can effectively embed unseen components and outperforms models that consider
inductive settings for KGs and baselines that directly use conventional KG
embedding methods.
