Abstract
This paper focuses on the design of knowledge intensive CBR systems and introduces a domain-independent architecture to help it. Our approach is based on acquiring the domain knowledge by reusing knowledge from a library of ontologies and integrating it with CBROnto, a task based ontology comprising common CBR terminology. In this paper we focus in retrieval and similarity assessment processes taking advantage of this domain knowledge. We describe our CBROnto based similarity representation framework and explain how it is used to represent similarity measures and retrieval processes.
Supported by the Spanish Committee of Science & Technology (CICYT TIC98-0733)
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Díaz-Agudo, B., González-Calero, P.A. (2001). A Declarative Similarity Framework for Knowledge Intensive CBR. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_12
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DOI: https://doi.org/10.1007/3-540-44593-5_12
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