Abstract
This paper shows how typicality can be used to improve the case retrieval of a case-based reasoning (CBR) system, improving at the same time the global results of the CBR system. Typicality discriminates subclasses of a class in the domain ontology depending of how a subclass is a good example for its class. Our approach proposes to partition the subclasses of some classes into atypical, normal and typical subclasses in order to refine the domain ontology. The refined ontology allows a finer-grained generalization of the query during the retrieval process. The benefits of this approach are presented according to an evaluation in the context of Taaable, a CBR system designed for the cooking domain.
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Notes
- 1.
Another way to determine the three sets of typicality is to cluster the values of \({{\texttt {typ}}}({\texttt {B}}_i, {\texttt {A}})\). The clustering method could, for example, use the k-means approach with \(k=3\). Some tests have been run to do so and they show only a little difference with the choice of the thresholds 0 and 0.5. Moreover, for the evaluation we present, this small threshold shifts do not impact the results.
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Gaillard, E., Lieber, J., Nauer, E. (2015). Improving Case Retrieval Using Typicality. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_12
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