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知识图谱在自动作业危害分析中的开发:模式。

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

机构信息

Sustainable Infrastructure and Resource Management, UniSA STEM, University of South Australia, Adelaide, SA 5000, Australia.

Department of Civil and Environmental Engineering, Hongik University, Seoul 04066, Republic of Korea.

出版信息

Sensors (Basel). 2023 Apr 11;23(8):3893. doi: 10.3390/s23083893.

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.

摘要

在当前的实践中,安全管理系统的一个重要组成部分——作业危害分析(JHA),是手动完成的,依赖于安全人员的经验知识和观察。本研究旨在创建一个新的本体,全面表示 JHA 知识领域,包括隐含知识。具体来说,分析了 115 份实际的 JHA 文件和对 18 名 JHA 领域专家的访谈,并将其用作创建新的 JHA 知识库的知识来源,即作业危害分析知识图(JHAKG)。为了确保开发的本体的质量,在这个过程中使用了一种名为 METHONTOLOGY 的系统的本体开发方法。为了验证目的而进行的案例研究表明,JHAKG 可以作为一个知识库运行,回答关于危害、外部因素、风险水平以及减轻风险的适当控制措施的查询。由于 JHAKG 是一个代表大量先前开发的实际 JHA 案例的知识库,也是尚未以任何明确形式正式化的隐含知识,因此从数据库查询生成的 JHA 文件的质量在完整性和全面性方面预计会比单个安全经理生成的文件更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0f8/10146431/de985b9107e1/sensors-23-03893-g006.jpg

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