Semantic embedding of knowledge graphs has been widely studied and used for
prediction and statistical analysis tasks across various domains such as
Natural Language Processing and the Semantic Web. However, less attention has
been paid to developing robust methods for embedding OWL (Web Ontology
Language) ontologies which can express a much wider range of semantics than
knowledge graphs and have been widely adopted in domains such as
bioinformatics. In this paper, we propose a random walk and word embedding
based ontology embedding method named OWL2Vec*, which encodes the semantics of
an OWL ontology by taking into account its graph structure, lexical information
and logical constructors. Our empirical evaluation with three real world
datasets suggests that OWL2Vec* benefits from these three different aspects of
an ontology in class membership prediction and class subsumption prediction
tasks. Furthermore, OWL2Vec* often significantly outperforms the
state-of-the-art methods in our experiments.

本文提出了一种基于随机游走和词嵌入的本体嵌入方法 OWL2Vec *，该方法将 OWL 本体的语义编码为结合了图结构，词汇信息和逻辑构造式的表示。通过在三个真实数据集上的实证评估，我们发现 OWL2Vec * 在类成员预测和类包含预测任务中从本体的三个不同方面受益，并且通常在我们的实验中优于现有的技术水平。