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Improving spare part search for maintenance services using topic modelling

Authors :
Grishina, Anastasiia
Stolikj, Milosh
Gao, Qi
Petkovic, Milan
Conrad, Stefan
Tiddi, Ilaria
Security
Eindhoven MedTech Innovation Center
EAISI Health
Source :
CIKMW2020 Proceeding of the CIKM 2020 Workshops: Proceedings of the CIKM 2020 Workshops co-located with 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), CIKMW2020 Proceeding of the CIKM 2020 Workshops
Publication Year :
2020
Publisher :
CEUR-WS.org, 2020.

Abstract

To support the decision-making process in various industrial applications, many companies use knowledge management and Information Retrieval (IR). In an industrial setting, knowledge is extracted from data that is often stored in a semi-structured or unstructured format. As a result, Natural Language Processing (NLP) methods have been applied to a number of IR steps. In this work, we explore how NLP and particularly topic modelling can be used to improve the relevance of spare part retrieval in the context of maintenance services. A proposed methodology extracts topics from short maintenance service reports that also include part replacement data. An intuition behind the proposed methodology is that every topic should represent a specific root cause. Experimental were conducted for an ad-hoc retrieval system of service case descriptions and spare parts. The results have shown that our modification improves a baseline system thus boosting the performance of maintenance service solution recommendation.

Details

Language :
English
Database :
OpenAIRE
Journal :
CIKMW2020 Proceeding of the CIKM 2020 Workshops: Proceedings of the CIKM 2020 Workshops co-located with 29th ACM International Conference on Information and Knowledge Management (CIKM 2020), CIKMW2020 Proceeding of the CIKM 2020 Workshops
Accession number :
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