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A Case Study on Ont...
A Case Study on Ontology Development for AI Based Decision Systems in Industry
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- D'Cruze, Ricky Stanley (author)
- Mälardalens universitet,Akademin för innovation, design och teknik
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- Ahmed, Mobyen Uddin, Dr, 1976- (author)
- Mälardalens universitet,Inbyggda system
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- Bengtsson, Marcus, 1977- (author)
- Mälardalens universitet,Innovation och produktrealisering,Volvo CE, Bolindervägen 100, 635 10, Eskilstuna, Sweden
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- Rehman, Atiq Ur (author)
- Mälardalens universitet,Inbyggda system
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- Funk, Peter, 1957- (author)
- Mälardalens universitet,Inbyggda system
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- Sohlberg, Rickard (author)
- Mälardalens universitet,Inbyggda system
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(creator_code:org_t)
- Springer Science and Business Media Deutschland GmbH, 2024
- 2024
- English.
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In: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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