TY  - JOUR
PY  - 2023//
TI  - Knowledge in graphs: investigating the completeness of industrial near miss reports
JO  - Safety science
A1  - Simone, Francesco
A1  - Ansaldi, Silvia Maria
A1  - Agnello, Patrizia
A1  - Di Gravio, Giulio
A1  - Patriarca, Riccardo
SP  - e106305
EP  - e106305
VL  - 168
IS  - 
N2  - 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>
LA  - en
SN  - 0925-7535
UR  - http://dx.doi.org/10.1016/j.ssci.2023.106305
ID  - ref1
ER  -