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Towards a knowledge graph framework for ad hoc analysis in manufacturing.

Authors :
Meyers, Bart
Vangheluwe, Hans
Lietaert, Pieter
Vanderhulst, Geert
Van Noten, Johan
Schaffers, Michel
Maes, Davy
Gadeyne, Klaas
Source :
Journal of Intelligent Manufacturing; Dec2024, Vol. 35 Issue 8, p3731-3752, 22p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09565515
Volume :
35
Issue :
8
Database :
Complementary Index
Journal :
Journal of Intelligent Manufacturing
Publication Type :
Academic Journal
Accession number :
180970390
Full Text :
https://doi.org/10.1007/s10845-023-02319-6