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GDKAT: A goal‐driven knowledge acquisition tool for knowledge base development

Chien‐Hsing Wu, Shu‐Chen Kao, K. Srihari

Open publisher page 12 citations

Abstract

While knowledge‐based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in‐depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain‐dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal‐driven knowledge acquisition tool (GDKAT) that helps elicit and store experts’ declarative and procedural knowledge in knowledge bases for a user‐defined domain. The designed tool is implemented using the object‐oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.

About this research paper

What this paper is about

While knowledge‐based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in‐depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain‐dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal‐driven knowledge acquisition tool (GDKAT) that helps elicit and store experts’ declarative and procedural knowledge in knowledge bases for a user‐defined domain. The designed tool is implemented using the object‐oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.

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OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

While knowledge‐based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in‐depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain‐dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal‐driven knowledge acquisition tool (GDKAT) that helps elicit and store experts’ declarative and procedural knowledge in knowledge bases for a user‐defined domain. The designed tool is implemented using the object‐oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.

Key concepts: Computer science, Knowledge acquisition, Domain knowledge, Knowledge base, Domain (mathematical analysis), Process (computing), Software engineering, Human–computer interaction

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