@article{ref1,
title="Knowledge in graphs: investigating the completeness of industrial near miss reports",
journal="Safety science",
year="2023",
author="Simone, Francesco and Ansaldi, Silvia Maria and Agnello, Patrizia and Di Gravio, Giulio and Patriarca, Riccardo",
volume="168",
number="",
pages="e106305-e106305",
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.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2023.106305",
url="http://dx.doi.org/10.1016/j.ssci.2023.106305"
}