OWL2Vec*: Embedding of OWL Ontologies
This addresses the need for robust ontology embedding in domains like bioinformatics, where OWL is widely used, representing an incremental improvement over existing knowledge graph embedding methods.
The paper tackles the problem of embedding OWL ontologies, which express broader semantics than knowledge graphs, by proposing OWL2Vec*, a method that encodes graph structure, lexical information, and logical constructors, resulting in significant outperformance over state-of-the-art methods in class membership and subsumption prediction tasks.
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.