Knowledge conceptualization tool

• Published in: IEEE transactions on knowledge and data engineering Vol. 9; no. 2; pp. 209 - 220
• Main Authors: Fujihara, H., Simmons, D.B., Ellis, N.C., Shannon, R.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.1997; IEEE Computer Society
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer Society; Computer systems performance. Reliability; Data engineering; Exact sciences and technology; Filters; Humans; Information retrieval; Knowledge acquisition; Knowledge based systems; Knowledge engineering; Learning and adaptive systems; Manuals; Prototypes; Software; System architecture; Prototype; Knowledge acquisition; Expert system; Knowledge base; Research; Experimental study; Implementation; System performance; Conceptualization; Formalization; Probabilistic model; Data structure; Artificial intelligence; Intelligent system
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/69.591447

Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process, and few automated support tools exist to help knowledge engineers enhance their performance. The paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process.