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Many large-scale knowledge bases simultaneously represent two views ofknowledge graphs (KGs): an ontology view for abstract and commonsense concepts,and an instance view for specific entities that are instantiated fromontological concepts. Existing KG embedding models, however, merely focus onrepresenting one of the two views alone. In this paper, we propose a noveltwo-view KG embedding model, JOIE, with the goal to produce better knowledgeembedding and enable new applications that rely on multi-view knowledge. JOIEemploys both cross-view and intra-view modeling that learn on multiple facetsof the knowledge base. The cross-view association model is learned to bridgethe embeddings of ontological concepts and their corresponding instance-viewentities. The intra-view models are trained to capture the structured knowledgeof instance and ontology views in separate embedding spaces, with ahierarchy-aware encoding technique enabled for ontologies with hierarchies. Weexplore multiple representation techniques for the two model components andinvestigate with nine variants of JOIE. Our model is trained on large-scaleknowledge bases that consist of massive instances and their correspondingontological concepts connected via a (small) set of cross-view links.Experimental results on public datasets show that the best variant of JOIEsignificantly outperforms previous models on instance-view triple predictiontask as well as ontology population on ontologyview KG. In addition, our modelsuccessfully extends the use of KG embeddings to entity typing with promisingperformance.

Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts