Reaching consensus over contradictory interpretation of Semantic Web data for ontology mapping

Software agents that need to interpret the possible meaning of Semantic Web data should be able to deal with scenarios where the different agent's belief becomes contradicting. This is especially true for ontology mapping where different agents using different similarity measures create beliefs in the assessed similarities and this needs to be combined into a more coherent state. 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.