Languages for Knowledge Acquisition: Building and Extending Models

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Citation: Mark A. Musen. (1989) Languages for Knowledge Acquisition: Building and Extending Models. In KSL-89-07, 1989.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Mark A. Musen
title Languages for Knowledge Acquisition: Building and Extending Models
number KSL-89-07
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 1989
Bibtex more
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abstract Knowledge acquisition often is described as a process whereby expertise is"transferred" from the minds of application specialists to those of knowledge engineers, and thence to the knowledge bases of expert systems. This popular view, however, is misleading. Knowledge acquisition is a creative and inventive activity in which system builders generate new computational models of intelligent behavior (Winograd & Flores, 1986). In building an expert system, developers first construct a general model of the application task to be performed; that model then is validated and revised as necessary, as the experts and the knowledge engineers attempt to fit their evolving model to specific application problems. Knowledge acquisition thus can be viewed as comprising two phases: (1) building a general task model--that is, creating an INTENTION; followed by (2) establishing the content knowledge in the domain that corroborates the general model--that is, creating an EXTENSION (Addis,1987). In this paper, I shall discuss the special nature of these two phases of knowledge acquisition, and shall argue for the use of knowledge-system development tools that are specialized for these two discrete aspects of the expert-system life-cycle.

KSL Technical Report ID: KSL-89-07
Facts about Languages for Knowledge Acquisition: Building and Extending ModelsRDF feed
Abstract Knowledge acquisition often is described a … Knowledge acquisition often is described as a process whereby expertise is"transferred" from the minds of application specialists to those of knowledge engineers, and thence to the knowledge bases of expert systems. This popular view, however, is misleading. Knowledge acquisition is a creative and inventive activity in which system builders generate new computational models of intelligent behavior (Winograd & Flores, 1986). In building an expert system, developers first construct a general model of the application task to be performed; that model then is validated and revised as necessary, as the experts and the knowledge engineers attempt to fit their evolving model to specific application problems. Knowledge acquisition thus can be viewed as comprising two phases: (1) building a general task model--that is, creating an INTENTION; followed by (2) establishing the content knowledge in the domain that corroborates the general model--that is, creating an EXTENSION (Addis,1987). In this paper, I shall discuss the special nature of these two phases of knowledge acquisition, and shall argue for the use of knowledge-system development tools that are specialized for these two discrete aspects of the expert-system life-cycle. e aspects of the expert-system life-cycle.
Address Stanford, CA, USA  +
Author Mark A. Musen  +
Bibtype techreport  +
Has author Mark A. Musen  +
Has identifier KSL-89-07  +
Has publishing details 1989  +
Has title Languages for Knowledge Acquisition: Building and Extending Models  +
Has where published KSL-89-07  +
Has year 1989  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-89-07  +
Number KSL-89-07  +
Process note YES  +
Title Languages for Knowledge Acquisition: Building and Extending Models  +
Year 1989  +
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