Populating ontology graphs represents a long-standing problem for the
Semantic Web community. Recent advances in translation-based graph embedding
methods for populating instance-level knowledge graphs lead to promising new
approaching for the ontology population problem. However, unlike instance-level
graphs, the majority of relation facts in ontology graphs come with
comprehensive semantic relations, which often include the properties of
transitivity and symmetry, as well as hierarchical relations. These
comprehensive relations are often too complex for existing graph embedding
methods, and direct application of such methods is not feasible. Hence, we
propose On2Vec, a novel translation-based graph embedding method for ontology
population. On2Vec integrates two model components that effectively
characterize comprehensive relation facts in ontology graphs. The first is the
Component-specific Model that encodes concepts and relations into
low-dimensional embedding spaces without a loss of relational properties; the
second is the Hierarchy Model that performs focused learning of hierarchical
relation facts. Experiments on several well-known ontology graphs demonstrate
the promising capabilities of On2Vec in predicting and verifying new relation
facts. These promising results also make possible significant improvements in
related methods.

提出了 On2Vec，它是一种新的基于翻译的图形嵌入方法，可以有效地解决本体填充问题。 On2Vec 集成了两个模型组件，可以有效地特征化本体图中的全面关系事实。 在多个知名本体图上的实验表明，On2Vec 可以预测和验证新的关系事实。