Nagy, Miklos; Vargas-Vera, Maria and Motta, Enrico
Managing conflicting beliefs with fuzzy trust on the Semantic Web.
In: The 7th Mexican International Conference on Artificial Intelligence (MICAI 2008), 27-31 Oct 2008, Mexico City, Mexico.
Full text available as:
Due to copyright restrictions, this file is not available for public download
Automated ontology mapping approaches often combine similarity measures in order to increase the quality of the proposed mappings. When the mapping process of human experts is modeled with software agents that assess similarities, it can lead to situations where the beliefs in the assessed similarities becomes contradicting. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. Typically mapping algorithms, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different sources. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.
Actions (login may be required)