Multi agent trust for belief combination on the Semantic Web
Miklós Nagy, María Vargas-Vera, Enrico Motta · 2008
Software agents that assess similarities between concepts on the semantic Web has to deal with scenarios 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.