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 contain richer semantic information than plain 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.

知识图谱的语义嵌入已被广泛研究并用于跨自然语言处理和语义网等各个领域的预测和统计分析任务。然而，开发用于嵌入 OWL（Web 本体语言）本体的鲁棒方法的关注较少，该方法包含比普通知识图更丰富的语义信息，并已被广泛应用于生物信息学等领域。在本文中，我们提出了一种名为OWL2Vec* 的基于随机游走和词嵌入的本体嵌入方法，该方法通过考虑其图结构、词汇信息和逻辑构造函数来对 OWL 本体的语义进行编码。我们对三个真实世界数据集的实证评估表明OWL2Vec*在类成员预测和类包含预测任务中受益于本体的这三个不同方面。此外，OWL2Vec*在我们的实验中通常明显优于最先进的方法。