Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35862
Title: A knowledge extraction and representation system for narrative analysis in the construction industry
Authors: Yeung, CL
Cheung, CF 
Wang, WM
Tsui, E 
Keywords: Construction industry
Knowledge management
Knowledge representation
Knowledge extraction
Narrative analysis
Issue Date: 2014
Publisher: Pergamon Press
Source: Expert systems with applications, 2014, v. 41, no. 13, p. 5710-5722 How to cite?
Journal: Expert systems with applications 
Abstract: Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students' learning.
URI: http://hdl.handle.net/10397/35862
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2014.03.044
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