OWL2Vec*: embedding of OWL ontologies

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