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Computational narrative mapping for the acquisition and representation of lessons learned knowledge

Yeung, C.L., Wang, W.M., Cheung, C.F., Tsui, Eric, Setchi, Rossitza and Lee, Rongbin W.B. 2018. Computational narrative mapping for the acquisition and representation of lessons learned knowledge. Engineering Applications of Artificial Intelligence 71 , pp. 190-209. 10.1016/j.engappai.2018.02.011

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Abstract

Lessons learned knowledge is traditionally gained from trial and error or narratives describing past experiences. Learning from narratives is the preferred option to transfer lessons learned knowledge. However, learners with insufficient prior knowledge often experience difficulties in grasping the right information from narratives. This paper introduces an approach that uses narrative maps to represent lessons learned knowledge to help learners understand narratives. Since narrative mapping is a time-consuming, labor-intensive and knowledge-intensive process, the proposed approach is supported by a computational narrative mapping (CNM) method to automate the process. CNM incorporates advanced technologies, such as computational linguistics and artificial intelligence (AI), to identify and extract critical narrative elements from an unstructured, text-based narrative and organize them into a structured narrative map representation. This research uses a case study conducted in the construction industry to evaluate CNM performance in comparison with existing paragraph and concept mapping approaches. Among the results, over 90% of respondents asserted that CNM enhanced their understanding of the lessons learned. CNM’s performance in identifying and extracting narrative elements was evaluated through an experiment using real-life narratives from a reminiscence study. The experiment recorded a precision and recall rate of over 75%.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0952-1976
Date of First Compliant Deposit: 13 April 2018
Date of Acceptance: 19 February 2018
Last Modified: 01 Jul 2019 10:03
URI: http://orca.cf.ac.uk/id/eprint/110017

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