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Conceptual models for automatic generation of knowledge-acquisition tools

Published online by Cambridge University Press:  07 July 2009

Henrik Eriksson  and
Mark A. Musen
Henrik Eriksson
Affiliation:
Medical Computer Science Group, Knowledge Systems Laboratory, Stanford University School of Medicine, Stanford, CA 94305-5479, USA
Mark A. Musen
Affiliation:
Medical Computer Science Group, Knowledge Systems Laboratory, Stanford University School of Medicine, Stanford, CA 94305-5479, USA
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Abstract

Interactive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta-tools provide developers of domain-specific KA tools with generic design models, or meta-views, of the emerging KA tools. The same KA tool can be specified according to several alternative meta-views.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 8 , Issue 1 , March 1993 , pp. 27 - 47
Copyright
Copyright © Cambridge University Press 1993

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References

Barbuceanu, M, 1990. “The MODELS environment for knowledge acquisition: Automating the task-specific architecture approach” In: Boose, JH and Gaines, BR (eds.), Proceedings 5th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp 1.1–1.20.Google Scholar
Bennett, A, 1990. FormIKA: A form-based user interface management system for knowledge acquisition. Technical Report KSL-90-43, Knowledge Systems Laboratory, Stanford University, Stanford, CA.Google Scholar
Bennett, JS, 1985. “ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system” Journal of Automated Reasoning 1 (1) 49–74.CrossRefGoogle Scholar
Boose, JH, 1985. “A knowledge acquisition program for expert systems based on personal construct psychology” International Journal of Man-Machine Studies 23 (5) 495–525.CrossRefGoogle Scholar
Boose, JH and Bradshaw, JM, 1987. “Expertise transfer and complex problems: Using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems” International Journal of Man–Machine Studies 26 (1) 3–28.Google Scholar
Chandrasekaran, B, 1986. “Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design” IEEE Expert 1 (3) 23–30.CrossRefGoogle Scholar
Clancey, WJ, 1985. “Heuristic classification” Artificial Intelligence 27 (3) 289–350.CrossRefGoogle Scholar
Davis, R, 1979. “Interactive transfer of expertise: Acquisition of new inference rules” Artificial Intelligence 12 (2) 121–157.CrossRefGoogle Scholar
Eriksson, H, 1991a. “Architectural issues in KA tools: Towards structured transformation into knowledge-bases” In: Proceedings 5th European Knowledge Acquisition for Knowledge-Based Systems Workshop, Crieff, Scotland.Google Scholar
Eriksson, H, 1991b. Meta-Tool Support for Knowledge Acquisition, PhD thesis, Linköping University, Sweden.Google Scholar
Eriksson, H, 1992. “Domain-oriented knowledge acquisition tool for protein purification planning” Journal of Chemical Information and Computer Sciences 32 (1) 90–95.Google Scholar
Eshelman, L, Ehret, D, McDermott, J and Tan, M, 1987. “MOLE: A tenacious knowledge-acquisition tool” International Journal of Man–Machine Studies 26 (1) 41–54.Google Scholar
Evans, R, 1990. “Expert systems and HyperCard” Byte 15 (1) 317–324.Google Scholar
Gale, WA, 1987. “Knowledge-based knowledge acquisition for a statistical consulting system” Internaional Journal of Man–Machine Studies 26 (1) 55–64.CrossRefGoogle Scholar
Gappa, U, 1991. “A tool-box for generating graphical knowledge acquisition environments” In: Proceedings of the World Congress on Expert Systems, Orlando, FL.Google Scholar
Kahn, G, Nowlan, S and McDermott, J, 1985. “Strategies for knowledge acquisition” IEEE Transactions on Pattern Analysis and Machine Intelligence 7 (5) 511–522.CrossRefGoogle ScholarPubMed
Karbach, W, Linster, M and Voβ, A, 1990. “Models, methods, roles and tasks: Many labels—one idea?” Knowledge Acquisition 2 (4) 279–299.Google Scholar
Kawaguchi, A, Motoda, H and Mizoguchi, R, 1991. “Interview-based knowledge acquisition using dynamic analysis” IEEE Expert 6 (5) 47–60.Google Scholar
Klinker, G, Bentolila, J, Genetet, S., Grimes, M and McDermott, J., 1987. “KNACK: report-driven knowledge acquisition” International Journal of Man–Machine Studies 26 (1) 65–79.CrossRefGoogle Scholar
Klinker, G, Bhola, C, Dallemagne, G, Marques, D and McDermott, J, 1991. “Usable and reusable programming constructs” Knowledge Acquisition 3 (2) 117–135.CrossRefGoogle Scholar
Marcus, S and McDermott, J, 1989. “SALT: A knowledge acquisition language for propose-and-revise systems” Artificial Intelligence 39 (1) 1–37.CrossRefGoogle Scholar
Marques, D, Klinker, G, Dallemagne, G, Gautier, P, McDermott, J and Tung, D, 1991. “More data on usable and reusable programming constructs” In: Boose, JH and Gaines, BR (eds.), Proceedings 6th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp 14.1–14.19.Google Scholar
McCarthy, J and Hayes, PJ, 1969. “Some philosophical problems from the standpoint of artificial intelligence” Machine Intelligence 4 463–502.Google Scholar
McDermott, J, 1988. “Preliminary steps toward a taxonomy of problem-solving methods” In: Marcus, S (ed.), Automating Knowledge Acquisition for Expert Systems, pp 225–256, Kluwer.CrossRefGoogle Scholar
Motta, E, Rajan, T and Eisenstadt, M, 1990. “Knowledge acquisition as a process of model refinement” Knowledge Acquisition 2 (1) 21–49.Google Scholar
Musen, MA, 1989a. Automated Generation of Model-Based Knowledge-Acquisition Tools, Morgan-Kaufmann.Google Scholar
Musen, MA, 1989b. “Conceptual models of interactive knowledge acquisition tools” Knowledge Acquisition 1 (1) 73–88.Google Scholar
Musen, MA, 1989c. “An editor for the conceptual models of interactive knowledge acquisition tools” International Journal of Man–Machine Studies 31 (6) 673–698.Google Scholar
Musen, MA, Fagan, LM, Combs, DM and Shortliffe, EH, 1987. “Use of a domain model to drive an interactive knowledge-editing tool” International Journal of Man–Machine Studies 26 (1) 105–121.CrossRefGoogle Scholar
Newell, A, 1982. “The knowledge level” Artificial Intelligence 18 (1) 87–127.CrossRefGoogle Scholar
Puerta, AR, Egar, JW and Musen, MA, 1991. Automated generation of adaptable knowledge-acquisition tools with Mecano. Technical Report KSL-91-62, Knowledge Systems Laboratory, Stanford University, Stanford, CA.Google Scholar
Puerta, AR, Egar, JW, Tu, SW and Musen, MA, 1992. “A multiple-method knowledge-acquisition shell for the automatic generation of knowledge-acquisition tools” Knowledge Acquisition 4 (2) 171–196.Google Scholar
Tu, SW, Kahn, MG, Musen, MA, Ferguson, JC, Shortliffe, EH and Fagan, LM, 1989. “Episodic skeletal-plan refinement based on temporal data” Communications of the ACM 32 (12) 1439–1455.CrossRefGoogle Scholar
Wielinga, BJ, Schreiber, AT and Breuker, JA, 1992. “KADS: a modelling approach to knowledge engineering” Knowledge Acquisition 4 (1) 5–53.Google Scholar