Abstract
Although CBR has been advertised as a technique to elude knowledge engineering (KE), we argue that knowledge-level modeling in KE is of eminent importance to the success of CBR systems, both for practical and theoretical reasons. Cases are knowledge structures linked to some underlying database (although not necessarily in a one-to-one fashion), and in order to define case structures and their relations to the database, domain knowledge is needed. In this paper, we focus on KE for CBR in the domain of architectural design, first looking at general analyses of work processes and information use, then discussing microanalyses of task structure in order to define case size, finally proceeding to knowledge-level evaluation of the domain knowledge acquired and modeled so far.
This research was supported by the Federal Ministry of Education, Science, Research and Technology (BMBF), within the joint project FABEL under contract 01-IW-104-D7. Project partners in FABEL are the German National Research Center for Computer Science (GMD), Sankt Augustin, BSR Consulting GmbH, München, the Technical University of Dresden, the HTWK Leipzig, the University of Freiburg, and the University of Karlsruhe.
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Strube, G., Enzinger, A., Janetzko, D., Knauff, M. (1995). Knowledge engineering for CBR systems from a cognitive science perspective. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_51
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DOI: https://doi.org/10.1007/3-540-60598-3_51
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