Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data

  • Miklos Nagy
  • Maria Vargas-Vera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7123)


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.


Membership Function Multiagent System Resource Description Framework Combination Rule Trust Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Miklos Nagy
    • 1
  • Maria Vargas-Vera
    • 2
  1. 1.Knowledge Media Institute (KMi)The Open UniversityMilton KeynesUnited Kingdom
  2. 2.Facultad de Ingenieria y Ciencias Centro de Investigaciones en Informatica y TelecomunicacionesUniversidad Adolfo IbanezChile

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