How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability

Emmanuelle Gaillard 1, 2, 3 Jean Lieber 1, 2, 3 Emmanuel Nauer 3, 1, 2 Amélie Cordier 4, 5
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper shows that performing case-based reasoning (CBR) on knowledge coming from an e-community is improved by taking into account knowledge reliability. MKM (meta-knowledge model) is a model for managing reliability of the knowledge units that are used in the reasoning process. For this, MKM uses meta-knowledge such as belief, trust and reputation, about knowledge units and users. MKM is used both to select relevant knowledge to conduct the reasoning process, and to rank results provided by the CBR engine according to the knowledge reliability. An experiment in which users perform a blind evaluation of results provided by two systems (with and without taking into account reliability, i.e. with and without MKM) shows that users are more satisfied with results provided by the system implementing MKM.
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Communication dans un congrès
Case-Based Reasoning Research and Development, Sep 2014, Cork, Ireland, Ireland. 8765, pp.155 - 169, 2014, 〈http://link.springer.com/chapter/10.1007%2F978-3-319-11209-1_12〉. 〈10.1007/978-3-319-11209-1_12〉
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Emmanuelle Gaillard, Jean Lieber, Emmanuel Nauer, Amélie Cordier. How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability. Case-Based Reasoning Research and Development, Sep 2014, Cork, Ireland, Ireland. 8765, pp.155 - 169, 2014, 〈http://link.springer.com/chapter/10.1007%2F978-3-319-11209-1_12〉. 〈10.1007/978-3-319-11209-1_12〉. 〈hal-01082369〉

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