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Towards a knowledge graph framework for ad hoc analysis in manufacturing

Author

Listed:
  • Bart Meyers

    (Flanders Make)

  • Hans Vangheluwe

    (Flanders Make at University of Antwerp)

  • Pieter Lietaert

    (Flanders Make)

  • Geert Vanderhulst

    (Flanders Make)

  • Johan Van Noten

    (Flanders Make)

  • Michel Schaffers

    (Flanders Make)

  • Davy Maes

    (Flanders Make)

  • Klaas Gadeyne

    (Flanders Make)

Abstract

The development of artificial intelligence models for data driven decision making has a lot of potential for the manufacturing sector. Nevertheless, applications in industry are currently limited to the actionable insights one can discover from the available data and knowledge of a manufacturing system. We call the process to obtain such insights “ad hoc analysis”. Ad hoc analysis at system level is very complex in an industrial setting due to the inherent heterogeneity of data and existence of data silos, the lack of information and knowledge formalization, and the inability to meaningfully and efficiently reason about the data, information and knowledge. In this paper, we provide and outline a framework for ad hoc analysis in manufacturing based on knowledge graphs and influenced by the metamodelling paradigm. We derive its requirements and key elements from an analysis of several industry application cases. We show how manufacturing data, information and knowledge can be combined and made actionable using this framework. The framework supports workflows and tools for the data consumer (i.e., data scientist), and for the knowledge engineer. Furthermore, we show how the framework is integrated with existing data sources. Then, we discuss how we applied the framework to several application cases. We discuss how the framework contributes when applied, and what challenges still remain.

Suggested Citation

  • Bart Meyers & Hans Vangheluwe & Pieter Lietaert & Geert Vanderhulst & Johan Van Noten & Michel Schaffers & Davy Maes & Klaas Gadeyne, 2024. "Towards a knowledge graph framework for ad hoc analysis in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3731-3752, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02319-6
    DOI: 10.1007/s10845-023-02319-6
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    References listed on IDEAS

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    1. Farhad Ameri & Dusan Sormaz & Foivos Psarommatis & Dimitris Kiritsis, 2022. "Industrial ontologies for interoperability in agile and resilient manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 60(2), pages 420-441, January.
    2. Foivos Psarommatis & João Sousa & João Pedro Mendonça & Dimitris Kiritsis, 2022. "Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 73-91, January.
    3. Xiaochen Zheng & Jinzhi Lu & Dimitris Kiritsis, 2022. "The emergence of cognitive digital twin: vision, challenges and opportunities," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7610-7632, December.
    4. Francisco J. García-Peñalvo & Patrica Ordónez de Pablos & Juan García & Roberto Therón, 2014. "Using OWL-VisMod through a decision-making process for reusing OWL ontologies," Behaviour and Information Technology, Taylor & Francis Journals, vol. 33(5), pages 426-442, May.
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