# An Embedding-Based Approach to Repairing OWL Ontologies
###### Abstract  
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. ontology repair; inconsistency handling; word embedding; knowledge graph +
Footnote †: journal: Applied  
to an unsatisfiable concept will produce inconsistency, which has negative impacts on Semantic Web applications including terminological reasoning, data transformation, and query answering [16; 17]. Therefore, it is paramount to handle such logical conflicts. To deal with incoherence or inconsistency, conflict detection and ontology repair become two critical tasks [18]. The former demonstrates why an ontology is incoherent or inconsistent, and the latter is to compute a diagnosis for regaining logical coherence or consistency. We mainly focus on repairing ontologies in this paper and refer the readers to the references [19; 20; 21] to obtain more details about detecting conflicts. To repair an ontology automatically, it is often required to locate the logical conflicts in the ontology first and then choose at least one axiom from each conflict as a diagnosis [22; 23; 24]. That is, removing all axioms in the diagnosis will regain logical coherence or consistency. When choosing axioms for deleting, various ranking strategies have been proposed to assign a degree to each axiom in conflict. Most of the existing strategies to rank axioms for ontology repair rely on the syntax of axioms while ignoring the semantics of axioms. For example, the works in [22; 25; 26] use a scoring function by computing the frequency of occurrence of each axiom in all conflicts. The work in [27] proposes to rank the axioms by considering the usages of each signature in an axiom. Relatively, the authors in [23] rank different diagnoses by aggregating the truth values of the axioms in a diagnosis, where a truth value is calculated by an embedding model. Their experimental results have shown that the proposed approach is significantly more effective than classical random methods in ranking the best diagnoses. Although this work employs an embedding model to encode axioms as vectors for preserving their semantics, it only pays more attention to simple axioms as triples while ignoring complex axioms expressed in different ontology languages. In this paper, we focus on dealing with incoherence in ontologies whose axioms can be much more complex than triples. Precisely, we propose an embedding-based approach that translates OWL axioms into natural language sentences, and then ranks the axioms in conflicts by employing the embeddings of such translated sentences. We further provide facilities to compute the semantic similarities among axioms. In this way, an axiom in a conflict could be associated with a degree by considering the semantic relationships between the axiom and others.