  Most researches for knowledge graph completion learn representations of
entities and relations to predict missing links in incomplete knowledge graphs.
However, these methods fail to take full advantage of both the contextual
information of entity and relation. Here, we extract contexts of entities and
relations from the triplets which they compose. We propose a model named AggrE,
which conducts efficient aggregations respectively on entity context and
relation context in multi-hops, and learns context-enhanced entity and relation
embeddings for knowledge graph completion. The experiment results show that
AggrE is competitive to existing models.
