Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.
In the past decades, expert systems have been applied to various applications. Subject domains that are supported by experts systems include bioengineering, defense, education, engineering, finance, and medical diagnosis. For example, MYCIN project is a well-known medical expert system for diagnosing infectious diseases (Buchanan & Shortliffe, 1985); ISODEPOR was developed to evaluate the muscle strength of Spanish top-competition athletes (Barreiro et al., 1997); FRBS-GP is a fuzzy rule-based system for diagnosing aphasia’s subtypes and the classification of pap-smear examinations (Jantzen, Axer, & Keyserlingk, 2002).
The successful cases of the expert system approach not only demonstrated the benefits of applying expert system approach to coping with medical diagnosis problems, but also depicted the difficulty of applying it. In building an expert system, the critical bottleneck is to obtain the knowledge of the special domain from the domain experts, which is called knowledge acquisition. In past decades, several methods and systems have been proposed to cope with this problem. However, most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Recent literature also indicated that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes a much more challenging and important issue to take the “time” factor into consideration (Hwang, Chen, Hwang, & Chu, 2006).
To cope with these problems, we shall propose a Delphi-based approach to eliciting knowledge from multiple experts. An application of developing a medical expert system has depicted the superiority of the novel approach.
In this study, we propose a knowledge acquisition method to elicit expertise from multiple experts, in which each element is considered to have one or more time scales; moreover, a systematical procedure is proposed to elicit embedded meanings based on the degree of relevance for each symptom to each time scale of elements. To evaluate the effectiveness of the novel approach, a knowledge acquisition system has been developed, and seven medical experts were asked to participate in an experiment. From the experimental results, it can be seen that our novel approach able to achieve significantly better performance, and hence, we conclude that the new approach is helpful in enhancing repertory grid efficacy. Now, we are planning to employ the novel approach to several issues concerning e-learning and medical education.
