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.
This work is supported by French National Agency for Research (ANR), program Contint 2011 through the Kolflow project. More information about Kolflow is available on the project website: http://kolflow.univ-nantes.fr/. The authors wish also to thank the persons who have participated to the evaluation, and especially students of the DUT Informatique of Université Lyon 1 and students of the Licence Informatique of Université de Lorraine.
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Gaillard, E., Lieber, J., Nauer, E., Cordier, A. (2014). How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_12
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DOI: https://doi.org/10.1007/978-3-319-11209-1_12
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