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A Case Study on Ontology Development for AI Based Decision Systems in Industry
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo CE, Bolindervägen 100, 635 10, Eskilstuna, Sweden.ORCID iD: 0000-0002-0729-0122
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 693-706Conference paper, Published paper (Refereed)
Abstract [en]

Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. p. 693-706
Keywords [en]
Custom NER, Industrial AI, Machine failures prediction, Ontology development, Artificial intelligence, Decision support systems, Interoperability, Iterative methods, Knowledge management, AI systems, Case-studies, Decision systems, Failures prediction, Machine failure, Machine failure prediction, Ontology's, Ontology
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-65369DOI: 10.1007/978-3-031-39619-9_51Scopus ID: 2-s2.0-85181980940ISBN: 9783031396182 (print)OAI: oai:DiVA.org:mdh-65369DiVA, id: diva2:1828606
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved

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D'Cruze, Ricky StanleyAhmed, Mobyen UddinBengtsson, MarcusRehman, Atiq UrFunk, PeterSohlberg, Rickard

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D'Cruze, Ricky StanleyAhmed, Mobyen UddinBengtsson, MarcusRehman, Atiq UrFunk, PeterSohlberg, Rickard
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School of Innovation, Design and EngineeringEmbedded SystemsInnovation and Product Realisation
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