An Argumentation Framework based on Confidence Degrees to Combine Ontology Mapping Approaches

Abstract

Ontology mapping has a key importance for applications such as information retrieval, database integration, and agent-communication. This paper presents an Argumentation Framework, with confidence degrees associated to the arguments, to combine ontology mapping approaches. Our agents apply individual mapping algorithms and cooperate in order to exchange their local results (arguments). 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. The model is evaluated using a benchmark for ontology mapping. The results are promising especially what concerns precision.