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International Conference on Case-Based Reasoning

ICCBR 2013: Case-Based Reasoning Research and Development pp 104–118Cite as

Case-Based Reasoning on E-Community Knowledge

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7969))

Abstract

This paper presents MKM, a meta-knowledge model to manage knowledge reliability, in order to extend a CBR system so that it can reason on partially reliable, non expert, knowledge from the Web. Knowledge reliability is considered from the point of view of the decision maker using the CBR system. It is captured by the MKM model including notions such as belief, trust, reputation and quality, as well as their relationships and rules to evaluate knowledge reliability. We detail both the model and the associated approach to extend CBR. Given a problem to solve for a specific user, reliability estimation is used to filter knowledge with high reliability as well as to rank the results produced by the CBR system, ensuring the quality of results.

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Author information

Authors and Affiliations

  1. LORIA, Université de Lorraine, 54506, Vandoeuvre-lès-Nancy, France

    Emmanuelle Gaillard, Jean Lieber & Emmanuel Nauer

  2. CNRS, 54506, Vandoeuvre-lès-Nancy, France

    Emmanuelle Gaillard, Jean Lieber & Emmanuel Nauer

  3. Inria, 54602, Villers-lès-Nancy, France

    Emmanuelle Gaillard, Jean Lieber & Emmanuel Nauer

  4. CRP Henri Tudor, Luxembourg

    Yannick Naudet

Authors
  1. Emmanuelle Gaillard
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  2. Jean Lieber
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  3. Yannick Naudet
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  4. Emmanuel Nauer
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Editor information

Editors and Affiliations

  1. School of Computing, Dublin Institute of Technology, Kevin Street, 8, Dublin, Ireland

    Sarah Jane Delany

  2. Department of Computer Science, Drexel University, 3141 Chestnut Street, 19104, Philadelphia, PA, USA

    Santiago Ontañón

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Gaillard, E., Lieber, J., Naudet, Y., Nauer, E. (2013). Case-Based Reasoning on E-Community Knowledge. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-39056-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39055-5

  • Online ISBN: 978-3-642-39056-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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