Managing knowledge in the construction industry through computational generation of semi-fiction narratives

Chui Ling Yeung, Chi Fai Cheung, Wai Ming Wang, Yue Hong Eric Tsui, Wing Bun Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Purpose – Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning. Design/methodology/approach – A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach. Findings – The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes. Originality/value – This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.
Original languageEnglish
Pages (from-to)386-414
Number of pages29
JournalJournal of Knowledge Management
Volume20
Issue number2
DOIs
Publication statusPublished - 4 Apr 2016

Keywords

  • Artificial intelligence
  • Construction industry
  • Knowledge management systems
  • Learning
  • Narratives
  • Training

ASJC Scopus subject areas

  • Strategy and Management
  • Management of Technology and Innovation

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