Using an Extended Argumentation Framework based on Confidence Degrees for Legal Core Ontology Mapping

Abstract

Web legal information retrieval systems use legal ontologies to represent semantic objects, to associate them with legal documents and to make inferences about them. The quality of the output of these systems can be improved with the ontology completeness, which can be obtained by the ontology merging process. The first step in this process is the ontology mapping. This paper proposes to use abstract argumentation frameworks to combine ontology mapping approaches. We extend the Value-based Argumentation Framework (VAF)[1], in order to represent arguments with confidence degrees. Our agents apply individual mapping algorithms and cooperate in order to exchange their local results (arguments). Next, based on their preferences and confidence of the arguments, the agents compute their preferred mapping sets. The arguments in such preferred sets are viewed as the set of globally acceptable arguments. We applied our model to map two legal core ontologies, LRI-Core and DOLCE-Lite, and to map LRI-Core with SUMO generic core ontology.