Citation

Simone F, Ansaldi SM, Agnello P, Di Gravio G, Patriarca R. Safety Sci. 2023; 168: e106305.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ssci.2023.106305

PMID

unavailable

Abstract

Learning from near misses has a large potential for improving operations especially in high-risk sectors, such as Seveso industries. A comprehensive analysis of near miss reports requires processing a large volume of data from various sources, which are not standardized and seemingly disconnected from each other. A knowledge graph is here used to provide a comprehensive safety perspective to near miss data. In particular, this paper presents an analysis of a knowledge graph for near miss reports with the objective to measure systematically their completeness based on an integrated multi-criteria decision-making technique. The reports completeness fosters a meta-analysis of available data, highlighting systems' strengths and vulnerabilities, as well as disseminating best practices for industry stakeholders.

Language: en

Keywords

Computational safety; Industrial plants; Refineries; Safety management; Safety ontology