Towards a knowledge graph framework for ad hoc analysis in manufacturing

Bart Meyers (), Hans Vangheluwe (), Pieter Lietaert (), Geert Vanderhulst (), Johan Van Noten (), Michel Schaffers (), Davy Maes () and Klaas Gadeyne ()
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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

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 8, 3752 pages

Abstract: 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.

Keywords: Knowledge graphs; Data analysis; Data management; Knowledge management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02319-6

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