Agents, trust, and information access on the semantic web
ACM SIGMOD Record
Mass Assignment Fundamentals for Computing with Words
IJCAI '97 Selected and Invited Papers from the Workshop on Fuzzy Logic in Artificial Intelligence
Trusting Information Sources One Citizen at a Time
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Fuzzy number approach to trust in coalition environment
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A fuzzy approach to reasoning with trust, distrust and insufficient trust
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Dealing with matching variability of semantic web data using contexts
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
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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.