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Ontology-based risk assessment in smelting plant logistics
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0009-0007-1151-4065
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0001-9379-4281
2025 (engelsk)Inngår i: HHAI 2025: Proceedings of the 4th international conference on hybrid human-artificial intelligence / [ed] Dino Pedreschi; Michela Milano; Ilaria Tiddi; Stuart Russell; Chiara Boldrini; Luca Pappalardo; Andrea Passerini; Shenghui Wang, IOS Press, 2025, s. 289-302Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents an ontology-based approach to proactive risk assessment in high-risk industrial environments, with a focus on molten metal handling in smelting plants. Distributed cognition theory serves as the theoretical framework, guiding the grounded theory and thematic analysis by framing collaboration and communication as emerging from interactions among operators, equipment, and the environment. This lens emphasized shared knowledge and distributed responsibilities, helping identify domain-relevant concepts critical for risk assessment. Expert insights from professionals at a Swedish smelting plant were systematically elicited and structured into an ontology to support proactive risk management. A prototype system integrating this ontology with a dynamic interface showed strong alignment with expert decision-making and safety assessments. Moreover, the system identified overlooked risks—such as hazardous equipment containing molten metal—and received positive user feedback. While tailored for smelting operations, the methodology has broader applicability to improving information sharing, decision-making, and safety in other socio-technical systems.

sted, utgiver, år, opplag, sider
IOS Press, 2025. s. 289-302
Serie
Frontiers in artficial intelligence and applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 408
Emneord [en]
ontologies, distributed cognition theory, knowledge representation, risk assessment, industrial safety
HSV kategori
Forskningsprogram
datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-243720DOI: 10.3233/FAIA250646Scopus ID: 2-s2.0-105020964302ISBN: 978-1-64368-611-0 (digital)OAI: oai:DiVA.org:umu-243720DiVA, id: diva2:1993547
Konferanse
The 4th International Conference on Hybrid Human-Artificial Intelligence, Pisa, Italy, June 9–13, 2025
Tilgjengelig fra: 2025-08-31 Laget: 2025-08-31 Sist oppdatert: 2025-11-24bibliografisk kontrollert

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Granström, JohnBrännström, Andreas

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