An Embedding-Based Approach to Repairing OWL Ontologies - Qiu Ji; Guilin Qi; Yinkai Yang; Weizhuo Li; Siying Huang; Yang Sheng (Applied Sciences, 2022)
High-quality ontologies are critical to ontology-based applications, such as natural language understanding and information extraction, but logical conflicts naturally occur in the lifecycle of ontology development. To deal with such conflicts, conflict detection and ontology repair become two critical tasks, and we focus on repairing ontologies. Most existing approaches for ontology repair rely on the syntax of axioms or logical consequences but ignore the semantics of axioms. In this paper, we propose an embedding-based approach by considering sentence embeddings of axioms, which translates axioms into semantic vectors and provides facilities to compute semantic similarities among axioms. A threshold-based algorithm and a signature-based algorithm are designed to repair ontologies with the help of detected conflicts and axiom embeddings. In the experiments, our proposed algorithms are compared with existing ones over 20 real-life incoherent ontologies. The threshold-based algorithm with different distance metrics is further evaluated with 10 distinct thresholds and 3 pre-trained models. The experimental results show that the embedding-based algorithms could achieve promising performances.