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Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema

In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel’s experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowle...

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Autores principales: Pandithawatta, Sonali, Ahn, Seungjun, Rameezdeen, Raufdeen, Chow, Christopher W. K., Gorjian, Nima, Kim, Tae Wan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146431/
https://www.ncbi.nlm.nih.gov/pubmed/37112233
http://dx.doi.org/10.3390/s23083893
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author Pandithawatta, Sonali
Ahn, Seungjun
Rameezdeen, Raufdeen
Chow, Christopher W. K.
Gorjian, Nima
Kim, Tae Wan
author_facet Pandithawatta, Sonali
Ahn, Seungjun
Rameezdeen, Raufdeen
Chow, Christopher W. K.
Gorjian, Nima
Kim, Tae Wan
author_sort Pandithawatta, Sonali
collection PubMed
description In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel’s experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowledge domain, including the implicit knowledge. Specifically, 115 actual JHA documents and interviews with 18 JHA domain experts were analyzed and used as the source of knowledge for creating a new JHA knowledge base, namely the Job Hazard Analysis Knowledge Graph (JHAKG). To ensure the quality of the developed ontology, a systematic approach to ontology development called METHONTOLOGY was used in this process. The case study performed for validation purposes demonstrates that a JHAKG can operate as a knowledge base that answers queries regarding hazards, external factors, level of risks, and appropriate control measures to mitigate risks. As the JHAKG is a database of knowledge representing a large number of actual JHA cases previously developed and also implicit knowledge that has not been formalized in any explicit forms yet, the quality of JHA documents produced from queries to the database is expectedly higher than the ones produced by an individual safety manager in terms of completeness and comprehensiveness.
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spelling pubmed-101464312023-04-29 Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema Pandithawatta, Sonali Ahn, Seungjun Rameezdeen, Raufdeen Chow, Christopher W. K. Gorjian, Nima Kim, Tae Wan Sensors (Basel) Article In the current practice, an essential element of safety management systems, Job Hazard Analysis (JHA), is performed manually, relying on the safety personnel’s experiential knowledge and observations. This research was conducted to create a new ontology that comprehensively represents the JHA knowledge domain, including the implicit knowledge. Specifically, 115 actual JHA documents and interviews with 18 JHA domain experts were analyzed and used as the source of knowledge for creating a new JHA knowledge base, namely the Job Hazard Analysis Knowledge Graph (JHAKG). To ensure the quality of the developed ontology, a systematic approach to ontology development called METHONTOLOGY was used in this process. The case study performed for validation purposes demonstrates that a JHAKG can operate as a knowledge base that answers queries regarding hazards, external factors, level of risks, and appropriate control measures to mitigate risks. As the JHAKG is a database of knowledge representing a large number of actual JHA cases previously developed and also implicit knowledge that has not been formalized in any explicit forms yet, the quality of JHA documents produced from queries to the database is expectedly higher than the ones produced by an individual safety manager in terms of completeness and comprehensiveness. MDPI 2023-04-11 /pmc/articles/PMC10146431/ /pubmed/37112233 http://dx.doi.org/10.3390/s23083893 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pandithawatta, Sonali
Ahn, Seungjun
Rameezdeen, Raufdeen
Chow, Christopher W. K.
Gorjian, Nima
Kim, Tae Wan
Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title_full Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title_fullStr Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title_full_unstemmed Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title_short Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema
title_sort development of a knowledge graph for automatic job hazard analysis: the schema
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10146431/
https://www.ncbi.nlm.nih.gov/pubmed/37112233
http://dx.doi.org/10.3390/s23083893
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