A primitives-based generic approach to knowledge acquisition

Chien, Chih-Cheng; Ho, Cheng-Seen

doi:10.1006/knac.1994.1012

Cited by 9 publications

(4 citation statements)

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“…In Ref. 19, we have conducted a survey on manual KA methods and interactive KA tools 20 ᎐ 23 and identified four basic interaction techniques as shown in Figure 8. Basically, the grid-dri¨en procedure technique is used in the repertory grid method, which elicits and analyzes the domain expert's world model based on personal construct theory and implemented in ETS 24 and AQUINAS.…”

Section: B Interaction Methodsmentioning

confidence: 99%

Generating domain knowledge for requirement analysis based on acquisition ontology

Lin

2000

Int. J. Intell. Syst.

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Section: B Interaction Methodsmentioning

confidence: 99%

Generating domain knowledge for requirement analysis based on acquisition ontology

Lin

2000

Int. J. Intell. Syst.

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“…Many techniques have been proposed, e.g., GAS [3], PROTEGE II [14] and KADS [17] to alleviate it. Most of them are meta-tools.…”

Section: Introductionmentioning

confidence: 99%

An intelligent hybrid system for knowledge acquisition

Hsu

1998

Lecture Notes in Computer Science

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Abstract. An intelligent hybrid system is proposed. It includes an adaptive human-machine interface and a hybrid case-based reasoning component for knowledge engineering. The adaptive human-machine interface remembers past question and answer scenarios between the system and the end users. It employs a KA-related commonsense base to help user interaction and a housekeeping engine to search and aggregate the data relevant to those the user answered. It discriminates the professional level of the user by the fuzziness of his answers, and provides different interaction patterns for the user accordingly. The hybrid case-based reasoning component hybridizes case-based reasoning, neural networks, fuzzy theory, induction, and knowledge-based reasoning technology. Hybridizing these techniques together properly enhances the robustness of the system, improves the knowledge engineering process, and promotes the quality of the developed knowledge-based systems. L IntroductionKnowledge acquisition (KA) has long been recognized as a very hard problem in knowledge engineering. Many techniques have been proposed, e.g., GAS [3], PROTEGE II [14] and KADS [17] to alleviate it. Most of them are meta-tools. The responsibility of the knowledge engineer is to select appropriate building blocks including problem solving methods, KA methods, knowledge representation methods, and inference methods. It is so easy for a junior knowledge engineer to be trapped in his stereotyped behavior during the selection of the building blocks that he is unable to fully exploit the power of the meta-tools. In general, the quality of the developed KA-tools using these meta-tools is heavily influenced by his experience.It looks a good idea that a technique that can accumulate knowledge engineering experience is introduced to help the knowledge engineers in using a meta-tool. Case-based reasoning is such a method. It relies on previous experience to solve a problem [7,10]; it comes up with new solutions by adapting the old solutions that have successfully solved previous similar problems. However, a knowledge engineering scenario usually involves much information. Which of it is significant enough in recognizing the previous cases for use is a big problem. How to maintain a case library so that it can be efficiently and effectively used poses another problem. In this paper, we propose a hybrid case-based system that integrates neural networks, induction, and knowledge-based technology to help learn how to focus on the problem and how to narrow down likely hypotheses in a way similar to the expert

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“…Despite increased activity in the 'non-human' aspects of knowledge acquisition in recent years, (Chien & Ho, 1994;Olson et al, 1995;Sommer et al, 1994), the use of, and research into, human expert-based KE continues (Bolger & Wright, 1994;Corbridge et al, 1994;Sandahl, 1994;Tecuci & Hieb, 1994;Moore et al, 1996;Shaw & Gaines, 1996;Sheremetyeva & Nirenburg, 1996), especially with regard to its validity as a component of a broader approach to knowledge acquisition, being used in conjunction with various other techniques (Donoho & Wilkins, 1994;Reinartz &Schmalhofer, 1994 andOlson et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

The Use of a Prototype System for Evaluating Knowledge Elicitation Techniques

Jones

Miles

1998

Expert Systems

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This paper describes the final phase of a research project aimed at investigating the efficacy of using two distinct methods of knowledge elicitation, comparing the knowledge obtained via classification or scaling techniques with that derived from the more traditional interview based approaches. It outlines the process of evaluating the knowledge elicited from an expert via a prototype specifically created for this purpose, the design of which was based directly on the findings of previous rapid prototyping approaches. The prototype was modelled to facilitate the evaluation of elicited knowledge along a numerical scale according to predetermined criteria. The use of the prototype resulted in the knowledge elicited via scaling methods receiving higher ratings than that obtained via non-scaling methods on the criteria of 'correctness' and 'relevance', although no significant difference was found between the two sets of data on the criterion of 'suitable for a knowledge based system'.

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A primitives-based generic approach to knowledge acquisition

Cited by 9 publications

References 0 publications

Generating domain knowledge for requirement analysis based on acquisition ontology

Generating domain knowledge for requirement analysis based on acquisition ontology

An intelligent hybrid system for knowledge acquisition

The Use of a Prototype System for Evaluating Knowledge Elicitation Techniques

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