Examining the adoption of knowledge graphs in the manufacturing industry: A comprehensive review

Jorge Martinez-Gil; Thomas Hoch; Mario Pichler; Bernhard Heinzl; Bernhard Moser; Kabul Kurniawan; Elmar Kiesling; Franz Krause

doi:10.1007/978-3-031-46452-2_4

Examining the adoption of knowledge graphs in the manufacturing industry: A comprehensive review

Jorge Martinez-Gil^*, Thomas Hoch, Mario Pichler, Bernhard Heinzl, Bernhard Moser, Kabul Kurniawan, Elmar Kiesling, Franz Krause

^*Korrespondierende*r Autor*in für diese Arbeit

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Sammelwerk

Abstract

The integration of Knowledge Graphs (KGs) in the manufacturing industry can significantly enhance the efficiency and flexibility of production lines and improve product quality. By integrating and contextualizing information about devices, equipment, production resources, location, usage, and related data, KGs can be a powerful operational tool. Moreover, KGs can contribute to the intelligence of manufacturing processes by providing insights into the complex and competitive manufacturing landscape. This research work presents a comprehensive analysis of the current trends utilizing KG in the manufacturing sector. We provide an overview of the state of the art in KG applications in manufacturing and highlight the critical issues that need to be addressed to enable a successful implementation. Our research aims to contribute to advancing KG technology in manufacturing and realizing its full potential to enhance manufacturing operations and competitiveness.

Originalsprache	Englisch
Titel des Sammelwerks	Artificial Intelligence in Manufacturing
Untertitel des Sammelwerks	Enabling Intelligent, Flexible and Cost-Effective Production Through AI
Verlag	Springer Nature
Seiten	55-70
Seitenumfang	16
ISBN (elektronisch)	9783031464522
ISBN (Print)	9783031464515
DOIs	https://doi.org/10.1007/978-3-031-46452-2_4
Publikationsstatus	Veröffentlicht - 8 Feb. 2024

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© The Author(s) 2024. All rights reserved.

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10.1007/978-3-031-46452-2_4

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Zitat

Martinez-Gil, J., Hoch, T., Pichler, M., Heinzl, B., Moser, B., Kurniawan, K., Kiesling, E., & Krause, F. (2024). Examining the adoption of knowledge graphs in the manufacturing industry: A comprehensive review. in Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI (S. 55-70). Springer Nature. https://doi.org/10.1007/978-3-031-46452-2_4

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author = "Jorge Martinez-Gil and Thomas Hoch and Mario Pichler and Bernhard Heinzl and Bernhard Moser and Kabul Kurniawan and Elmar Kiesling and Franz Krause",

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Martinez-Gil, J, Hoch, T, Pichler, M, Heinzl, B, Moser, B, Kurniawan, K , Kiesling, E & Krause, F 2024, Examining the adoption of knowledge graphs in the manufacturing industry: A comprehensive review. in Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Springer Nature, S. 55-70. https://doi.org/10.1007/978-3-031-46452-2_4

Examining the adoption of knowledge graphs in the manufacturing industry: A comprehensive review. / Martinez-Gil, Jorge; Hoch, Thomas; Pichler, Mario et al.
Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Springer Nature, 2024. S. 55-70.

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Sammelwerk

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T2 - A comprehensive review

AU - Martinez-Gil, Jorge

AU - Hoch, Thomas

AU - Pichler, Mario

AU - Heinzl, Bernhard

AU - Moser, Bernhard

AU - Kurniawan, Kabul

AU - Kiesling, Elmar

AU - Krause, Franz

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