Abstract
Knowledge Acquisition (KA) in AI consists in the transfer and transformation of knowledge from the forms in which it is available in the world into forms that can be used by a knowledge based system (KBS). Conventionally KA is done by AI specialists, called knowledge engineers, who interview domain experts and build prototype systems. This methodology is slow, expensive and error-prone. Thus, automating KA may provide domain experts with tools which reduce their need to have recourse to knowledge engineers.
Moreover, to have a KBS exhibiting an intelligent behavior, it must be endowed even with knowledge able to represent the expert’s strategies, other than with domain knowledge. The elicitation task is inherently difficult for strategic knowledge, even with traditional methods such as interviewing and rapid prototyping, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form that may be directly translated and implemented into a program. For this aim, tecniques need to be developed both to elicit knowledge from the expert user in a structured fashion and to represent it in an operational way, so that it can be easily implemented into a KBS avoiding any representation mismatch.
This paper describes a Medical Diagnostic Knowledge Acquisition Tool (MD-KAT) able to help an expert in the process of building KBSs in some medical domain. The tool is based on an epistemological model of diagnostic reasoning which has proved to be helpful in describing the diagnostic process in terms of the tasks by which it is composed of. This modeling of diagnostic reasoning at a knowledge level, together with its operationalization into the tool, helps in reducing the knowledge representation mismatch, and mostly in transferring even the expert strategy into the target KBS.
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© 1991 Springer-Verlag Berlin Heidelberg
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Lanzola, G., Stefanelli, M. (1991). A Knowledge Acquisition Tool for Medical Diagnostic Knowledge — Based Systems. In: Stefanelli, M., Hasman, A., Fieschi, M., Talmon, J. (eds) AIME 91. Lecture Notes in Medical Informatics, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48650-0_22
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DOI: https://doi.org/10.1007/978-3-642-48650-0_22
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