Pandithawatta S, Ahn S, Rameezdeen R, Chow CWK, Gorjian N, Kim TW. Development of a Knowledge Graph for Automatic Job Hazard Analysis: The Schema.
Sensors (Basel) 2023;
23:s23083893. [PMID:
37112233 PMCID:
PMC10146431 DOI:
10.3390/s23083893]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/18/2023] [Accepted: 04/07/2023] [Indexed: 06/12/2023]
Abstract
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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