Knowledge Acquisition from Multiple Experts: A Case of Transport Planning in Poland

1993 ◽  
pp. 261-274 ◽  
Author(s):  
Marian Tracz ◽  
Bronislaw Wawrzynkiewicz
Keyword(s):  
Knowledge Acquisition ◽  
Transport Planning ◽  
Multiple Experts

Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study

1996 ◽  
Vol 11 (3) ◽  
pp. 223-234
Author(s):  
Kathleen K. Molnar ◽  
Ramesh Sharda
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Electronic Mail ◽  
System Development ◽  
Cost Savings ◽  
Lessons Learned ◽  
The Internet ◽  
Domain Experts ◽  
Use Of The Internet ◽  
Multiple Experts

Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.

Development and Analysis of an Enhanced Multi-Expert Knowledge Integration System for Designing Context-Aware Ubiquitous Learning Contents

2018 ◽  
Vol 16 (4) ◽  
pp. 31-53 ◽  
Author(s):  
Gwo-Haur Hwang ◽  
Beyin Chen ◽  
Shiau-Huei Huang
Keyword(s):  
Knowledge Acquisition ◽  
Real World ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Expert Knowledge ◽  
Context Aware ◽  
Ubiquitous Learning ◽  
Integration System ◽  
Degree Of Acceptance ◽  
Multiple Experts

This article describes how in context-aware ubiquitous learning environments, teachers must plan a theme and design learning contents to provide complete knowledge for students. Knowledge acquisition, which is an approach for helping people represent and organize domain knowledge, has been recognized as a potential way of guiding teachers to develop real-world context-related learning contents. However, previous studies failed to address the issue that the learning contents provided by multiple experts or teachers might be redundant or inconsistent; moreover, it is difficult to use the traditional knowledge acquisition method to fully describe the complex real-world contexts and the learning contents. Therefore, in this article, a multi-expert knowledge integration system with an enhanced knowledge representation approach and Delphi method has been developed. From the experimental results, it is found that the teachers involved had a high degree of acceptance of the system. They believe that it can unify the knowledge of many teachers.

A HYBRID APPROACH TO KNOWLEDGE ACQUISITION USING NEURAL PETRI NETS AND DS THEORY

2001 ◽  
Vol 01 (02) ◽  
pp. 203-223 ◽  
Author(s):  
JAYA SIL ◽  
AMIT KONAR
Keyword(s):  
Knowledge Acquisition ◽  
Petri Nets ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Dempster Shafer Theory ◽  
Noisy Input ◽  
Novel Approach ◽  
Shafer Theory ◽  
Multiple Experts

Knowledge acquisition from multiple experts and its refinement are important issues in knowledge management of an expert system. The paper presents a novel approach to handling the above problems by combining the synergistic behavior of neural Petri nets and the Dempster–Shafer theory. The Dempster–Shafer theory has been employed here to reduce the scope of uncertainty in the supplied noisy input instances and the inferences generated therefrom by the multiple experts. The noise-free training instances thus obtained are subsequently used to train the neural Petri net model for refining the parameters of its knowledge base. A comparison of the performance of the proposed training algorithm with the classical back-propagation algorithm has also been presented in the paper.

Proposed Methodology for Knowledge Acquisition: A Study on Congenital Heart Disease Diagnosis

1990 ◽  
Vol 29 (01) ◽  
pp. 30-40 ◽  
Author(s):  
F. B. Leãot ◽  
F. A. Rocha
Keyword(s):  
Knowledge Acquisition ◽  
Congenital Heart ◽  
Heart Diseases ◽  
Disease Diagnosis ◽  
Congenital Heart Diseases ◽  
Entropy Analysis ◽  
Heuristic Rules ◽  
Human Power ◽  
Heart Disease Diagnosis ◽  
Multiple Experts

Abstract “Knowledge and human power are synonymous, since the ignorance of the cause frustrates the effect:…“ Francis Bacon1 This paper proposes a methodology for knowledge acquisition (KA) from multiple experts, in an attempt to elicit the heuristic rules followed by the physician in diagnosing twelve frequently occurring congenital heart diseases (CHD). Twenty-two pediatric cardiologists and twenty-three general cardiologists were interviewed with this technique; 274 interviews were conducted, 169 with the 22 experts, 105 with the 23 non-experts. A graph formalism was employed to represent their reasoning model, leading to the construction of a “mean reasoning model” for each diagnosis, separately for experts and non-experts. The results indicate that experts, compared to non-experts, tend to build knowledge representation models (KRM) that are smaller and less complex. Qualitative differences in information utilization between the two groups were also observed. Entropy analysis suggests a greater objectivity and cohesion of the experts’ model.

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