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Ontology-Based Approach to Supplier Risk Management Using Large Language Models

Zuha Shahid, Arnold Beckmann Orcid Logo, Abdourahim Sylla, Cinzia Giannetti Orcid Logo, Gülgün Alpan

IFAC-PapersOnLine, Volume: 59, Issue: 10, Pages: 2826 - 2831

Swansea University Authors: Arnold Beckmann Orcid Logo, Cinzia Giannetti Orcid Logo

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DOI (Published version): 10.1016/j.ifacol.2025.09.475

Abstract

Suppliers play a critical role in the efficient functioning of supply chains, and any risks associatedwith them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically...

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Published in: IFAC-PapersOnLine
ISSN: 2405-8963
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69399
first_indexed 2025-05-01T14:09:20Z
last_indexed 2025-10-03T05:55:03Z
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spelling 2025-10-02T14:29:05.4894661 v2 69399 2025-05-01 Ontology-Based Approach to Supplier Risk Management Using Large Language Models 1439ebd690110a50a797b7ec78cca600 0000-0001-7958-5790 Arnold Beckmann Arnold Beckmann true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2025-05-01 MACS Suppliers play a critical role in the efficient functioning of supply chains, and any risks associatedwith them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically addressing supplier risk management. In addition, the construction of ontologies has mainly relied on approaches which are time-consuming and resource-intensive. This paper bridges this gap with two major contributions: (i) A new methodology for ontology development that combines a Large Language Model (LLM) and a human expert to efficiently extract and organize domain knowledge from academic literature and (ii) A new supplier risk management ontology that formalizes knowledge related to supplier risk management. To evaluate its effectiveness, the proposed ontology is compared with one developed by a human expert to assess its completeness and accuracy. Journal Article IFAC-PapersOnLine 59 10 2826 2831 Elsevier BV 2405-8963 Supplier risk; Supplier Selection; Ontology; Knowledge management; Large language models; LLM 27 9 2025 2025-09-27 10.1016/j.ifacol.2025.09.475 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This work has been partially supported by the MIAI Multidisciplinary AI Institute at the Univ. Grenoble Alpes: (MIAI@Grenoble Alpes - ANR-19-P3IA-0003) 2025-10-02T14:29:05.4894661 2025-05-01T15:02:35.7783538 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Zuha Shahid 1 Arnold Beckmann 0000-0001-7958-5790 2 Abdourahim Sylla 3 Cinzia Giannetti 0000-0003-0339-5872 4 Gülgün Alpan 5 69399__35228__893423ce940a435aa47e0ff67312ceac.pdf 69399.VoR.pdf 2025-10-02T14:24:30.3769395 Output 1086092 application/pdf Version of Record true Copyright © 2025 The Authors. This is an open access article under the CC BY-NC-ND license. true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Ontology-Based Approach to Supplier Risk Management Using Large Language Models
spellingShingle Ontology-Based Approach to Supplier Risk Management Using Large Language Models
Arnold Beckmann
Cinzia Giannetti
title_short Ontology-Based Approach to Supplier Risk Management Using Large Language Models
title_full Ontology-Based Approach to Supplier Risk Management Using Large Language Models
title_fullStr Ontology-Based Approach to Supplier Risk Management Using Large Language Models
title_full_unstemmed Ontology-Based Approach to Supplier Risk Management Using Large Language Models
title_sort Ontology-Based Approach to Supplier Risk Management Using Large Language Models
author_id_str_mv 1439ebd690110a50a797b7ec78cca600
a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv 1439ebd690110a50a797b7ec78cca600_***_Arnold Beckmann
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
author Arnold Beckmann
Cinzia Giannetti
author2 Zuha Shahid
Arnold Beckmann
Abdourahim Sylla
Cinzia Giannetti
Gülgün Alpan
format Journal article
container_title IFAC-PapersOnLine
container_volume 59
container_issue 10
container_start_page 2826
publishDate 2025
institution Swansea University
issn 2405-8963
doi_str_mv 10.1016/j.ifacol.2025.09.475
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Suppliers play a critical role in the efficient functioning of supply chains, and any risks associatedwith them can significantly impact supply chain performance. While numerous studies have developed ontologies for various supplier-related areas, there is a lack of focus on ontologies specifically addressing supplier risk management. In addition, the construction of ontologies has mainly relied on approaches which are time-consuming and resource-intensive. This paper bridges this gap with two major contributions: (i) A new methodology for ontology development that combines a Large Language Model (LLM) and a human expert to efficiently extract and organize domain knowledge from academic literature and (ii) A new supplier risk management ontology that formalizes knowledge related to supplier risk management. To evaluate its effectiveness, the proposed ontology is compared with one developed by a human expert to assess its completeness and accuracy.
published_date 2025-09-27T05:29:29Z
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score 11.096027

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