The Knowledge Acquisition Grid: a method for training knowledge engineers

doi:10.1016/S0020-7373(87)80094-9

International Journal of Man-Machine Studies

Volume 26, Issue 2, February 1987, Pages 245-255

https://doi.org/10.1016/S0020-7373(87)80094-9 Get rights and content

This paper describes the Knowledge Acquisition Grid, developed to assist knowledge engineers in the manual transfer of expertise. The Grid is used in a knowledge-acquisition module which itself is part of a larger program designed to train people in knowledge engineering techniques offered by Digital Equipment Corporation. The Grid describes a two-dimensional space in which five forms of expert knowledge and six basic types of interview questions constitute the horizontal and vertical dimensions respectively. Description of the rationale, dimensions, components, and strategy for use of the Grid in the knowledge-acquisition component of building an expert system is provided along with discussion of the need for greater attention in general to the social psychology of expert interviewing.

References (14)

H.L. Chiesi et al.
Acquisition of domain-related information in relation to high and low domain knowledge
Journal of Verbal Learning and Verbal Behavior
(1979)
E.F. Loftus
Leading questions and the eyewitness report
Cognitive Psychology
(1975)
D.A. Waterman et al.
Protocol analysis as a task for artificial intelligence
Artificial Intelligence
(1971)
R.P. Abelson
The psychological status of the script concept
American Psychologist
(1981)
J.R. Anderson
M.T.H. Chi et al.
Expertise in problem solving
H.H. Clark
Language use and language users

There are more references available in the full text version of this article.

Cited by (36)

Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems
2022, Expert Systems with Applications
In critical infrastructure applications, timely and consistent fault detection and diagnosis is an increasingly important operational process, especially in the energy sector where safety is of the utmost importance. To realise this, engineers have to manually analyse data acquired from several assets using predefined diagnostic processes, but this is a time-consuming process requiring significant amounts of specialist expert knowledge. Data-driven approaches to support fault detection and diagnosis, and other similar problems, can produce accurate results comparable to what the engineers can achieve in a fraction of the time. However, the majority of these data-driven techniques are black box techniques and lack explainability which is often necessary for explaining decisions about critical assets in the power generation industry. Knowledge-based systems, such as rule-based expert systems have been shown to provide not only accurate decisions but also the explanation and reasoning behind these decisions in some related applications. However, there is a significant time cost associated with the development of knowledge-based systems, and in particular with the knowledge elicitation process, where the domain expert’s knowledge is formalised and is encoded into the system. This challenge is commonly referred to as the knowledge elicitation bottleneck.
In this paper, we present a novel approach to performing the knowledge elicitation using a set of symbolic primitives (rise, fall, fluctuate, and stable) to parameterise typical time-series condition monitoring data. The knowledge is represented by using a common language that can easily be communicated with (and from) the domain experts. This allows for the quick and accurate elicitation of the domain experts knowledge, but also the formalisation and implementation of the knowledge into a rapidly produced diagnostic system. Further to this, due to the parametrisation of the knowledge, it is possible to iteratively improve the knowledge base by updating these parameters based on new unseen data. This approach was applied to the Tennessee Eastman dataset, which is simulated data of a real-world industrial process. It was found that by using this approach it was possible to accurately and quickly capture the knowledge required to detect several faults within the case study dataset, but also provided fully explained reasons why each fault was detected by relating the explanations to the symbolic primitives previously defined.
Varieties of knowledge elicitation techniques
1994, International Journal of Human - Computer Studies
Information on knowledge elicitation methods is widely scattered across the fields of psychology, business management, education, counseling, cognitive science, linguistics, philosophy, knowledge engineering and anthropology. The purpose of this review is to (1) identify knowledge elicitation techniques and the associated bibliographic information, (2) organize the techniques into categories on the basis of methodological similarity, and (3) summarize for each category of techniques strengths, weaknesses, and recommended applications. The review is intended to provide a starting point for those interested in applying or developing knowledge elicitation techniques, as well as for those more generally interested in exploring the scope of the available methodology.
Development and implementation of a knowledge acquisition methodology for crop management expert systems
1993, Computers and Electronics in Agriculture
This paper presents methodology developed for knowledge acquisition for crop management expert systems. The proposed methodology is described through an extended waterfall model for knowledge acquisition. The way in which the methodology was implemented is presented, and the experience gained is discussed. Although the methodology has evolved through the development of an expert system for cucumber seedling production, it can be used for other crops. A field prototype of this expert system was implemented and is currently being tested in a real environment.
Modeling expert knowledge
1990, Knowledge Acquisition
The main difficulties in knowledge acquisition from domain experts stem from the variety of forms of knowledge, the various representations of knowledge, and the problems in making these explicit and accessible. There is, at present, no systematic overall methodological framework for knowledge acquisition to guide the organization and arrangement of the appropriate application of the many manual and automated techniques and methods used for knowledge acquisition. In considering these problems it is appropriate to draw on studies in cognitive science and associated disciplines to examine the models of the expert and the demands and goals of the task. This paper develops the modeling processes involved from the perspective of the expert trying to communicate his view of a target system and transfer it into computer implementable form. It identifies the distinct processes of elicitation, analysis and implementation, the knowledge representations of the intermediate knowledge bases which can be used to help the expert review and refine his conceptual model, and the computer knowledge bases which may be unrecognizable by the expert as related to his developing models. Finally, several methods of knowledge aquisition are reviewed in the context of these models.
The role of questioning in knowledge engineering and the interface of expert systems
1990, Poetics
This article discusses the role of questioning in two important parts of expert system design: knowledge elicitation and the interface of expert system shells. We discuss these in the context of an expert system for scriptwriting which assists in plot construction. Concerning knowledge elicitation, we propose a 6-stage method which is based on a model of question answering. The six stages in this method are: (1) becoming familiar with the topic, (2) free generation of key concepts, (3) question answering on information from the free generation task, (4) problem solving in the context of a particular exemplar, (5) exemplar generation, and (6) modifying and critiquing the prototype. Regarding the interfaces of expert system shells, we discuss the question-answering facilities on current expert systems. We discuss the inadequacies of current question-answering facilities and propose some suggestions that incorporate theories of question answering in cognitive science.
Using Tacit Expert Knowledge to Support Shop-floor Operators Through a Knowledge-based Assistance System
2023, Computer Supported Cooperative Work: CSCW: An International Journal

View all citing articles on Scopus

View full text

The Knowledge Acquisition Grid: a method for training knowledge engineers

Journal of Verbal Learning and Verbal Behavior

Cognitive Psychology

Artificial Intelligence

The psychological status of the script concept

American Psychologist

Expertise in problem solving

Language use and language users