Abstract
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.
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Notes
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spaCy 3 is an open-source natural language processing library for Python that provides streamlined and efficient tools for text processing and annotation, including tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and text classification. https://spacy.io/.
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Protégé is a free, open-source platform for knowledge management and computational linguistics, commonly used for building ontologies and knowledge graphs. https://protege.stanford.edu/.
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Acknowledgements
This work was supported by the Adapt 2030 project (Adaptive lifecycle design by applying digitalization and AI techniques to production) under Vinnova (Sweden's innovation agency) project grant 2019-05589 within the strategic innovation programme for Production 2030.
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D’Cruze, R.S., Ahmed, M.U., Bengtsson, M., Ur Rehman, A., Funk, P., Sohlberg, R. (2024). A Case Study on Ontology Development for AI Based Decision Systems in Industry. In: Kumar, U., Karim, R., Galar, D., Kour, R. (eds) International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-39619-9_51
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