  Inductive knowledge graph completion has been considered as the task of
predicting missing triplets between new entities that are not observed during
training. While most inductive knowledge graph completion methods assume that
all entities can be new, they do not allow new relations to appear at inference
time. This restriction prohibits the existing methods from appropriately
handling real-world knowledge graphs where new entities accompany new
relations. In this paper, we propose an INductive knowledge GRAph eMbedding
method, InGram, that can generate embeddings of new relations as well as new
entities at inference time. Given a knowledge graph, we define a relation graph
as a weighted graph consisting of relations and the affinity weights between
them. Based on the relation graph and the original knowledge graph, InGram
learns how to aggregate neighboring embeddings to generate relation and entity
embeddings using an attention mechanism. Experimental results show that InGram
outperforms 14 different state-of-the-art methods on varied inductive learning
scenarios.
