Modelling work domain knowledge with the combined use of abstraction hierarchy and living systems theory
Ham, Dong-Han 
ORCID: https://orcid.org/0000-0003-2908-057X
(2015)
Modelling work domain knowledge with the combined use of abstraction hierarchy and living systems theory.
Cognition, Technology and Work, 17
(4)
.
pp. 575-591.
ISSN 1435-5558
[Article]
(doi:10.1007/s10111-015-0338-y)
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Abstract
This study is aimed at developing a new method for modelling work domain knowledge with the combined use of abstraction hierarchy (AH) and living systems theory (LST). AH has been widely used as a work domain knowledge representation framework in the field of cognitive systems engineering and human–computer interaction, and its usefulness has been proved in a range of work domains. However, its effective use still remains a challenging issue. In order to address this problem, this study firstly points out several issues that can be raised in the use of AH and then explains why and how LST can give concepts and principles helpful to resolve them. The proposed method offers a framework for how to combine AH and LST, particularly to identify functional knowledge at higher abstraction levels. It also offers a process for modelling the knowledge of a work domain based on the combined use of AH and LST. The use of the proposed method is exemplified by modelling the knowledge of a simplified secondary cooling system of nuclear power plants. The proposed method is a new approach to refining the concepts of AH and modelling the knowledge of a work domain that humans should interact. It is believed that it will be a useful tool for knowledge modellers in identifying and modelling the knowledge of a work domain in terms of its functional structure. However, it should be noted that its usefulness can be limited to technology-oriented engineering systems; it would not be easily applied to human activity-oriented systems.
| Item Type: | Article |
|---|---|
| Additional Information: | First published online: 25 April 2015 |
| Research Areas: | A. > School of Science and Technology > Computer Science |
| Item ID: | 19290 |
| Notes on copyright: | This is a post-peer-review, pre-copyedit version of an article published in Cognition, Technology & Work. The final publication is available at Springer via http://doi.org/10.1007/s10111-015-0338-y |
| Useful Links: | |
| Depositing User: | Dong-Han Ham |
| Date Deposited: | 15 Apr 2016 09:32 |
| Last Modified: | 04 Mar 2023 09:12 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/19290 |
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