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Insights from a text mining survey on Expert Systems research from 2000 to 2016

Cortez, Paulo; Rita, Paulo; Moro, Sérgio; King, David and Hall, Jon (2018). Insights from a text mining survey on Expert Systems research from 2000 to 2016. Expert Systems: The Journal of Knowledge Engineering, article no. e12280.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1111/exsy.12280
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Abstract

This study presents a literature analysis using a semiautomated text mining and topic modelling approach of the body of knowledge encompassed in 17 years (2000–2016) of literature published in the Wiley's Expert Systems journal, a key reference in Expert Systems (ESs) research, in a total of 488 research articles.

The methodological approach included analysing countries from authors' affiliations, with results emphasizing the relevance of both U.S. and U.K. researchers, with Chinese, Turkish, and Spanish holding also a significant relevance. As a result of the sparsity found on the keywords, one of our goals became to devise a taxonomy for future submissions under 2 core dimensions: ESs' methods and ESs' applications. Finally, through topic modelling, data‐driven methods were unveiled as the most relevant, pairing with evaluation methods in its application to managerial sciences, arts, and humanities. Findings also show that most of the application domains are well represented, including health, engineering, energy, and social sciences.

Item Type: Journal Item
Copyright Holders: 2018 John Wiley & Sons, Ltd
ISSN: 0266-4720
Keywords: Expert Systems, literature analysis, research categorization, research evolution, text mining
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 53974
Depositing User: Jon Hall
Date Deposited: 27 Mar 2018 14:11
Last Modified: 24 May 2018 14:05
URI: http://oro.open.ac.uk/id/eprint/53974
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