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Risk information recommendation for engineering workers.

Martin, Kyle; Liret, Anne; Wiratunga, Nirmalie; Owusu, Gilbert; Kern, Mathias

Authors

Anne Liret

Gilbert Owusu

Mathias Kern



Contributors

Max Bramer
Editor

Miltos Petridis
Editor

Abstract

Within any sufficiently expertise-reliant and work-driven domain there is a requirement to understand the similarities between specific work tasks. Though mechanisms to develop similarity models for these areas do exist, in practice they have been criticised within various domains by experts who feel that the output is not indicative of their viewpoint. In field service provision for telecommunication organisations, it can be particularly challenging to understand task similarity from the perspective of an expert engineer. With that in mind, this paper demonstrates a similarity model developed from text recorded by engineer’s themselves to develop a metric directly indicative of expert opinion. We evaluate several methods of learning text representations on a classification task developed from engineers' notes. Furthermore, we introduce a means to make use of the complex and multi-faceted aspect of the notes to recommend additional information to support engineers in the field.

Citation

MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Risk information recommendation for engineering workers. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 311-325. Available from: https://doi.org/10.1007/978-3-030-04191-5_27

Conference Name 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018)
Conference Location Cambridge, UK
Start Date Dec 11, 2018
End Date Dec 13, 2018
Acceptance Date Sep 3, 2018
Online Publication Date Nov 16, 2018
Publication Date Dec 31, 2018
Deposit Date Jan 21, 2019
Publicly Available Date Jan 21, 2019
Publisher Springer
Pages 311-325
Series Title Lecture notes in computer science
Series Number 11311
Series ISSN 0302-9743
Book Title Artificial intelligence XXXV
ISBN 9783030041908
DOI https://doi.org/10.1007/978-3-030-04191-5_27
Keywords Case based reasoning; Information retrieval; Machine learning; Metric learning; Similarity modelling; Deep metric learning
Public URL http://hdl.handle.net/10059/3270

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