  Embedding entities and relations into a continuous multi-dimensional vector
space have become the dominant method for knowledge graph embedding in
representation learning. However, most existing models ignore to represent
hierarchical knowledge, such as the similarities and dissimilarities of
entities in one domain. We proposed to learn a Domain Representations over
existing knowledge graph embedding models, such that entities that have similar
attributes are organized into the same domain. Such hierarchical knowledge of
domains can give further evidence in link prediction. Experimental results show
that domain embeddings give a significant improvement over the most recent
state-of-art baseline knowledge graph embedding models.
