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Populating ontology graphs represents a long-standing problem for theSemantic Web community. Recent advances in translation-based graph embeddingmethods for populating instance-level knowledge graphs lead to promising newapproaching for the ontology population problem. However, unlike instance-levelgraphs, the majority of relation facts in ontology graphs come withcomprehensive semantic relations, which often include the properties oftransitivity and symmetry, as well as hierarchical relations. Thesecomprehensive relations are often too complex for existing graph embeddingmethods, and direct application of such methods is not feasible. Hence, wepropose On2Vec, a novel translation-based graph embedding method for ontologypopulation. On2Vec integrates two model components that effectivelycharacterize comprehensive relation facts in ontology graphs. The first is theComponent-specific Model that encodes concepts and relations intolow-dimensional embedding spaces without a loss of relational properties; thesecond is the Hierarchy Model that performs focused learning of hierarchicalrelation facts. Experiments on several well-known ontology graphs demonstratethe promising capabilities of On2Vec in predicting and verifying new relationfacts. These promising results also make possible significant improvements inrelated methods.

On2Vec: Embedding-based Relation Prediction for Ontology Population