Umeå University's logo

umu.sePublications
Operational message
There are currently operational disruptions. Troubleshooting is in progress.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ontology-based risk assessment in smelting plant logistics
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0009-0007-1151-4065
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-9379-4281
2025 (English)In: 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, p. 289-302Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IOS Press, 2025. p. 289-302
Series
Frontiers in artficial intelligence and applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 408
Keywords [en]
ontologies, distributed cognition theory, knowledge representation, risk assessment, industrial safety
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-243720DOI: 10.3233/FAIA250646Scopus ID: 2-s2.0-105020964302ISBN: 978-1-64368-611-0 (electronic)OAI: oai:DiVA.org:umu-243720DiVA, id: diva2:1993547
Conference
The 4th International Conference on Hybrid Human-Artificial Intelligence, Pisa, Italy, June 9–13, 2025
Available from: 2025-08-31 Created: 2025-08-31 Last updated: 2025-11-24Bibliographically approved

Open Access in DiVA

fulltext(746 kB)15 downloads
File information
File name FULLTEXT02.pdfFile size 746 kBChecksum SHA-512
2455682b47ef7bf889231ab4f7ebd76665f5073d82e481b213aede44d1c7bafc05114090be60318d6162215ff03a3c32b46d6cc873a43abe974cfb0690a13c06
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Granström, JohnBrännström, Andreas

Search in DiVA

By author/editor
Granström, JohnBrännström, Andreas
By organisation
Department of Computing Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 77 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 383 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf