  Ontology revision aims to seamlessly incorporate a new ontology into an
existing ontology and plays a crucial role in tasks such as ontology evolution,
ontology maintenance, and ontology alignment. Similar to repair single
ontologies, resolving logical incoherence in the task of ontology revision is
also important and meaningful, because incoherence is a main potential factor
to cause inconsistency and reasoning with an inconsistent ontology will obtain
meaningless answers.To deal with this problem, various ontology revision
approaches have been proposed to define revision operators and design ranking
strategies for axioms in an ontology. However, they rarely consider axiom
semantics which provides important information to differentiate axioms. In
addition, pre-trained models can be utilized to encode axiom semantics, and
have been widely applied in many natural language processing tasks and
ontology-related ones in recent years.Therefore, in this paper, we study how to
apply pre-trained models to revise ontologies. We first define four scoring
functions to rank axioms based on a pre-trained model by considering various
information from an ontology. Based on the functions, an ontology revision
algorithm is then proposed to deal with unsatisfiable concepts at once. To
improve efficiency, an adapted revision algorithm is designed to deal with
unsatisfiable concepts group by group. We conduct experiments over 19 ontology
pairs and compare our algorithms and scoring functions with existing ones.
According to the experiments, our algorithms could achieve promising
performance.
