Handling conflicts in uncertain ontologies using deductive argumentation

Ontologies can represent knowledge in a structured and formally well-understood way, which is crucial for information sharing. However, in practice, it is often difficult to have an error-free ontology. Confficts can occur due to modeling errors or ontology merging and evolution. Moreover, uncertainty can happen because of modeling choices or the lack of confidence for a constructed ontology. Argumentation frameworks for knowledge bases reasoning and management have received extensive interests in the field of Artificial Intelligence in recent years. In this paper, we propose a unified framework to handle conffiicts in uncertain ontologies with the use of deductive argumentation. Different from existing approaches, we introduce a stronger notion of con.ict that covers both inconsistency and incoherence, where the la.er is a special contradiction that can occur in an ontology. The unified approach spreads uncertainty degrees throughout argumentation trees and the enriched argument structure leads us to two novel inference relations. We then present a method to compute (counter)-Arguments as well as argumentation trees in the context of uncertain ontologies based on the developments of three notions called minimal confficting subontologies, maximal noncon.icting subontologies, and prudent justifications.