  Conventional Knowledge Graph Completion (KGC) assumes that all test entities
appear during training. However, in real-world scenarios, Knowledge Graphs (KG)
evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and
we need to represent these entities efficiently. Most existing Knowledge Graph
Embedding (KGE) methods cannot represent OOKG entities without costly
retraining on the whole KG. To enhance efficiency, we propose a simple and
effective method that inductively represents OOKG entities by their optimal
estimation under translational assumptions. Given pretrained embeddings of the
in-knowledge-graph (IKG) entities, our method needs no additional learning.
Experimental results show that our method outperforms the state-of-the-art
methods with higher efficiency on two KGC tasks with OOKG entities.
