  Many large-scale knowledge bases simultaneously represent two views of
knowledge graphs (KGs): an ontology view for abstract and commonsense concepts,
and an instance view for specific entities that are instantiated from
ontological concepts. Existing KG embedding models, however, merely focus on
representing one of the two views alone. In this paper, we propose a novel
two-view KG embedding model, JOIE, with the goal to produce better knowledge
embedding and enable new applications that rely on multi-view knowledge. JOIE
employs both cross-view and intra-view modeling that learn on multiple facets
of the knowledge base. The cross-view association model is learned to bridge
the embeddings of ontological concepts and their corresponding instance-view
entities. The intra-view models are trained to capture the structured knowledge
of instance and ontology views in separate embedding spaces, with a
hierarchy-aware encoding technique enabled for ontologies with hierarchies. We
explore multiple representation techniques for the two model components and
investigate with nine variants of JOIE. Our model is trained on large-scale
knowledge bases that consist of massive instances and their corresponding
ontological concepts connected via a (small) set of cross-view links.
Experimental results on public datasets show that the best variant of JOIE
significantly outperforms previous models on instance-view triple prediction
task as well as ontology population on ontologyview KG. In addition, our model
successfully extends the use of KG embeddings to entity typing with promising
performance.
