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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data
verfasst von : Miklos Nagy, Maria Vargas-Vera
Erschienen in: Uncertainty Reasoning for the Semantic Web II
Verlag: Springer Berlin Heidelberg
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Term similarity assessment usually leads to situations where contradictory evidence support has different views concerning the meaning of a concept and how similar it is to other concepts. Human experts can resolve their differences through discussion, whereas ontology mapping systems need to be able to eliminate contradictions before similarity combination can achieve high quality results. In these situations, different similarities represent conflicting ideas about the interpreted meaning of the concepts. Such contradictions can contribute to unreliable mappings, which in turn worsen both the mapping precision and recall. In order to avoid including contradictory beliefs in similarities during the combination process, trust in the beliefs needs to be established and untrusted beliefs should be excluded from the combination. In this chapter, we propose a solution for establishing fuzzy trust to manage belief conflicts using a fuzzy voting model.