OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings

Stress is a mental disorder responsible for impacting the industry through psychosomatic illnesses, loss of productivity, and accidents caused by stressful workplaces. Conversely, the literature indicates that fostering mental well-being among workers can boost motivation and performance while allev...

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Main Authors: Carlos Goetz, Rodrigo Simon Bavaresco, Wesllei Felipe Heckler, Gustavo Lazarotto Schroeder, Rafael Kunst, Jorge Luis Victória Barbosa
Format: Article
Language:English
Published: Graz University of Technology 2025-04-01
Series:Journal of Universal Computer Science
Subjects:
Online Access:https://lib.jucs.org/article/128779/download/pdf/
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author Carlos Goetz
Rodrigo Simon Bavaresco
Wesllei Felipe Heckler
Gustavo Lazarotto Schroeder
Rafael Kunst
Jorge Luis Victória Barbosa
author_facet Carlos Goetz
Rodrigo Simon Bavaresco
Wesllei Felipe Heckler
Gustavo Lazarotto Schroeder
Rafael Kunst
Jorge Luis Victória Barbosa
author_sort Carlos Goetz
collection DOAJ
description Stress is a mental disorder responsible for impacting the industry through psychosomatic illnesses, loss of productivity, and accidents caused by stressful workplaces. Conversely, the literature indicates that fostering mental well-being among workers can boost motivation and performance while alleviating symptoms of stress. The fourth industrial revolution incorporated technologies into work that allowed the automation of processes and control of environments. The fifth revolution introduced the application of research and innovation aimed at a human-centered consciousness, enabling the advancement of mental health through sensors and wearables. Despite advancements in stress classification technologies, there remain opportunities for further research into identifying stress motivators within industrial work environments. In this sense, this paper proposes an ontology to identify stressors considering personal and environmental data, allowing knowledge generation related to work stressors for mitigating the problem. The methodology utilized in this ontology development consisted of seven stages and two evaluation phases. The findings addressed four key questions related to competency as outlined in the model. The results revealed potential stressful scenarios, including the timing of occurrence, shared locations, environmental factors, and identifying groups experiencing moments of stress. This study presents as a scientific contribution the first ontology to address the identification of work-related stressors in the industrial environment.
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id doaj-art-a0396874efa845dfbd42a44a0107c636
institution OA Journals
issn 0948-6968
language English
publishDate 2025-04-01
publisher Graz University of Technology
record_format Article
series Journal of Universal Computer Science
spelling doaj-art-a0396874efa845dfbd42a44a0107c6362025-08-20T02:28:46ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682025-04-0131544546810.3897/jucs.128779128779OntoKaire: an ontology-based reasoning for work-related stressors in industrial settingsCarlos Goetz0Rodrigo Simon Bavaresco1Wesllei Felipe Heckler2Gustavo Lazarotto Schroeder3Rafael Kunst4Jorge Luis Victória Barbosa5University of Vale do Rio dos SinosUniversity of Vale do Rio dos SinosUniversity of Vale do Rio dos SinosUniversity of Vale do Rio dos SinosUniversity of Vale do Rio dos SinosUniversity of Vale do Rio dos SinosStress is a mental disorder responsible for impacting the industry through psychosomatic illnesses, loss of productivity, and accidents caused by stressful workplaces. Conversely, the literature indicates that fostering mental well-being among workers can boost motivation and performance while alleviating symptoms of stress. The fourth industrial revolution incorporated technologies into work that allowed the automation of processes and control of environments. The fifth revolution introduced the application of research and innovation aimed at a human-centered consciousness, enabling the advancement of mental health through sensors and wearables. Despite advancements in stress classification technologies, there remain opportunities for further research into identifying stress motivators within industrial work environments. In this sense, this paper proposes an ontology to identify stressors considering personal and environmental data, allowing knowledge generation related to work stressors for mitigating the problem. The methodology utilized in this ontology development consisted of seven stages and two evaluation phases. The findings addressed four key questions related to competency as outlined in the model. The results revealed potential stressful scenarios, including the timing of occurrence, shared locations, environmental factors, and identifying groups experiencing moments of stress. This study presents as a scientific contribution the first ontology to address the identification of work-related stressors in the industrial environment.https://lib.jucs.org/article/128779/download/pdf/Industry 5.0Mental HealthOntologyStressWor
spellingShingle Carlos Goetz
Rodrigo Simon Bavaresco
Wesllei Felipe Heckler
Gustavo Lazarotto Schroeder
Rafael Kunst
Jorge Luis Victória Barbosa
OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
Journal of Universal Computer Science
Industry 5.0
Mental Health
Ontology
Stress
Wor
title OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
title_full OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
title_fullStr OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
title_full_unstemmed OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
title_short OntoKaire: an ontology-based reasoning for work-related stressors in industrial settings
title_sort ontokaire an ontology based reasoning for work related stressors in industrial settings
topic Industry 5.0
Mental Health
Ontology
Stress
Wor
url https://lib.jucs.org/article/128779/download/pdf/
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