Recommending Words Using a Bayesian Network

Santos, Pedro; Pato, Matilde; Datia, Nuno; Sobral, José; Leitão, Noel; Ramos Ferreira, Manuel; Gomes, Nuno

doi:10.3390/electronics12102218

Open AccessArticle

Recommending Words Using a Bayesian Network

by

Pedro Santos

^1,*,†

,

Matilde Pato

^1,2,3,*,†

,

Nuno Datia

^1,3,4

,

José Sobral

^1,5

,

Noel Leitão

⁶,

Manuel Ramos Ferreira

⁶ and

Nuno Gomes

¹

ISEL, Lisbon School of Engineering, Instituto Politécnico de Lisboa, 1959-007 Lisboa, Portugal

²

LASIGE, FCUL, Faculty of Sciences of the University of Lisbon, 1600-277 Lisboa, Portugal

³

FIT-ISEL, 1959-007 Lisboa, Portugal

⁴

NOVA LINCS, NOVA School of Science and Technology, 2829-516 Monte da Caparica, Portugal

⁵

CENTEC, IST, Technical University of Lisbon, University of Lisbon, 1049-001 Lisboa, Portugal

⁶

TDGI—Property Management Technology, 2740-265 Porto Salvo, Portugal

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Electronics 2023, 12(10), 2218; https://doi.org/10.3390/electronics12102218

Submission received: 27 March 2023 / Revised: 4 May 2023 / Accepted: 11 May 2023 / Published: 12 May 2023

(This article belongs to the Special Issue Visual Analytics, Simulation, and Decision-Making Technologies)

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Abstract

Asset management involves the coordinated activities of an organisation to derive value from assets, which may include physical assets. It encompasses activities related to design, construction, installation, operation, maintenance, renewal, and asset disposal. Asset management ensures the coordination of all activities, resources, and data related to physical assets. Recording and monitoring all maintenance activities is a key part of asset management, often done using work orders (WOs). Technicians typically create WOs using “free text”, which can result in missing or ungrammatical words, making it difficult to identify trends and analyse information. To standardise the terminology used for the same asset maintenance operation, this paper proposes a method that suggests words to technicians as they complete WOs. The word suggestion algorithm is based on past maintenance records, and a Bayesian network-based recommender system adapts to present needs verified by technicians using implicit user feedback. Implementing this system aims to normalise the terms used by technicians when filling in a WO. The corpus for this work comes from asset management records collected in a health facility in Portugal operated by a private company.

Keywords: tag recommender system; NLP; word embeddings; asset management; Bayesian network

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MDPI and ACS Style

Santos, P.; Pato, M.; Datia, N.; Sobral, J.; Leitão, N.; Ramos Ferreira, M.; Gomes, N. Recommending Words Using a Bayesian Network. Electronics 2023, 12, 2218. https://doi.org/10.3390/electronics12102218

AMA Style

Santos P, Pato M, Datia N, Sobral J, Leitão N, Ramos Ferreira M, Gomes N. Recommending Words Using a Bayesian Network. Electronics. 2023; 12(10):2218. https://doi.org/10.3390/electronics12102218

Chicago/Turabian Style

Santos, Pedro, Matilde Pato, Nuno Datia, José Sobral, Noel Leitão, Manuel Ramos Ferreira, and Nuno Gomes. 2023. "Recommending Words Using a Bayesian Network" Electronics 12, no. 10: 2218. https://doi.org/10.3390/electronics12102218

APA Style

Santos, P., Pato, M., Datia, N., Sobral, J., Leitão, N., Ramos Ferreira, M., & Gomes, N. (2023). Recommending Words Using a Bayesian Network. Electronics, 12(10), 2218. https://doi.org/10.3390/electronics12102218

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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