  Knowledge graphs (KGs) consisting of a large number of triples have become
widespread recently, and many knowledge graph embedding (KGE) methods are
proposed to embed entities and relations of a KG into continuous vector spaces.
Such embedding methods simplify the operations of conducting various in-KG
tasks (e.g., link prediction) and out-of-KG tasks (e.g., question answering).
They can be viewed as general solutions for representing KGs. However, existing
KGE methods are not applicable to inductive settings, where a model trained on
source KGs will be tested on target KGs with entities unseen during model
training. Existing works focusing on KGs in inductive settings can only solve
the inductive relation prediction task. They can not handle other out-of-KG
tasks as general as KGE methods since they don't produce embeddings for
entities. In this paper, to achieve inductive knowledge graph embedding, we
propose a model MorsE, which does not learn embeddings for entities but learns
transferable meta-knowledge that can be used to produce entity embeddings. Such
meta-knowledge is modeled by entity-independent modules and learned by
meta-learning. Experimental results show that our model significantly
outperforms corresponding baselines for in-KG and out-of-KG tasks in inductive
settings.
