Abstract
This paper presents our knowledge-intensive Case-Based Reasoning platform for diagnosis, COBRA. It integrates domain knowledge along with cases in an ontological structure. COBRA allows users to describe cases using any concept or instance of a domain ontology, which leads to a heterogeneous case base. Cases heterogeneity complicates their retrieval since correspondences must be identified between query and case attributes. We present in this paper our system architecture and the case retrieval phase. Then, we introduce the notions of similarity regions and attributes’ roles used to overcome cases heterogeneity problems.
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Abou Assali, A., Lenne, D., Debray, B. (2009). Case Retrieval in Ontology-Based CBR Systems. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_71
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DOI: https://doi.org/10.1007/978-3-642-04617-9_71
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