Academic journal article Interdisciplinary Journal of Information, Knowledge and Management

The Underlying Issues in Knowledge Elicitation

Academic journal article Interdisciplinary Journal of Information, Knowledge and Management

The Underlying Issues in Knowledge Elicitation

Article excerpt


Knowledge Engineering, an activity in knowledge creation, is the process by which an engineer has to elicit knowledge out of an expert. This knowledge then needs to be modeled such that it could be represented as a set of rules in a ruled-based or expert system. Hsia, Lin, Wu, and Tsia (2006) described knowledge creation as identifying and selecting content from relevance, creating knowledge source catalog, capturing and discovering new knowledge. Knowledge elicitation is roundly defined by McGraw (1992) as the transfer and transformation of problem solving expertise and domain knowledge from a source into a program. Figure 1 shows the basic knowledge engineering model (Gaines & Shaw, 1995). Working with the domain expert(s), the knowledge engineer elicits, encodes and continuously refines the knowledgebase until an acceptable performance is achieved. A suitably designed shell uses the knowledgebase to draw inferences on cases that may be specified by users/clients. The inferences then help clients obtain advice on particular cases.

The knowledge elicitation problem entails the hindrances or discouragements encountered when prompting people to divulge what they know in their area of specialization. Essentially, these are the impediments to extracting or obtaining useful knowledge from experts for use in developing knowledge-based systems. These problems are as old as expert systems development itself which dates back to the mid 1960's.


To elicit knowledge consists of defining the main problem, including participants, characteristics, resources and goals.

All knowledge is not quickly accessible and knowledge as symbolic descriptions is especially very difficult to capture (Garavelli, Gorgoglione, & Scozzi, 2002). Furthermore, a clear distinction has to be made between explicit knowledge (that can be verbalized) and implicit knowledge (that is tacit). In fact, Nonaka (1994) and Nonaka and Takeuchi (1995) posit that the process of knowledge conversion (the dynamic interrelationship between tacit and explicit knowledge--Figure 2) lies at the heart of knowledge creation. Binotto, Hamer, Nakayama, and Silveira (2004) presented an analysis of this process in agricultural properties from the perspective of producers.

Research into knowledge management aims at capturing the tacit knowledge residing in heads of experts and making them explicit for general use. Tacit knowledge is essentially the driving force behind such innovations as new technologies, processes or techniques (Maqsood, Finegan, & Walker, 2004).

The creation of knowledge starts with an individual and spirals through successive conversion modes as shown in Figure 2. This essentially results in its amplification as large number of individuals, groups, and eventually the organization relate to the newly created knowledge (Vat, 2003).

Repetition of certain tasks over time usually results in the execution of such tasks without need for conscious thought. Also verbalizations may not be valid descriptions of real processes especially for those very difficult for an expert to verbalize. Interviews may also encourage experts to speculate about their cognitive processes. Again, a number of studies have shown that people can display consistent and accurate behaviour without being able to report verbally the concepts utilized. These points essentially constitute the knowledge elicitation bottleneck and must be kept in mind by the knowledge engineer when eliciting knowledge.


There are two broad classes of knowledge elicitation--manual and automated (or machine learning). This article initially considers start-up and in-elicitation issues for manual elicitation of implicit and explicit knowledge. This is followed by suggestions on determining suitable elicitation method(s) for different applications. Issues in automated elicitation as well as problems of knowledge analysis and transfer are also treated. …

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