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Towards Knowledge Graphs for Industrial End-To-End Data Integration: Technologies, Architectures and Potentials

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Production at the Leading Edge of Technology (WGP 2021)

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Abstract

Knowledge Graphs can be characterized as a set of semantically linked information artifacts. One of their possible applications is to enable the integration of heterogeneous data sources: A task becoming increasingly important within industry to obtain end-to-end transparency for complex process chains. There, they can further serve the data for automated machine learning algorithms to operate on. This paper at first elaborates on the definition and characteristics of Knowledge Graphs and data integration. Then, the underlying semantic architectures as well as the corresponding information technology standards are collected. Additionally, first industrial applications in the context of Knowledge Graph-based data integration are presented. The insights are compared with an exemplary custom implementation. Lastly, the findings are assessed for their potential to enable end-to-end data integration and furthermore to provide the basis for future machine learning applications to operate on.

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Acknowledgements

This paper emerged within the context of the publically funded research project “IDEA”. Funding agency: BMBF. Funding identifier: 13N15003.

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Authors and Affiliations

  1. Institute for Factory Automation and Production Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

    M. Sjarov & J. Franke

Authors
  1. M. Sjarov
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  2. J. Franke
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Corresponding author

Correspondence to M. Sjarov .

Editor information

Editors and Affiliations

  1. Institute of Forming Technology and Machines, Garbsen, Germany

    Bernd-Arno Behrens

  2. Institute of Manufacturing Science and Engineering, Dresden, Germany

    Alexander Brosius

  3. Institute for Machine Tools and Forming Technology, Chemnitz, Germany

    Welf-Guntram Drossel

  4. Institute of Production Management and Technology, Hamburg, Germany

    Wolfgang Hintze

  5. Institute of Mechatronic Engineering, Dresden, Germany

    Steffen Ihlenfeldt

  6. Institute of Production Systems and Logistics, Garbsen, Germany

    Peter Nyhuis

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Sjarov, M., Franke, J. (2022). Towards Knowledge Graphs for Industrial End-To-End Data Integration: Technologies, Architectures and Potentials. In: Behrens, BA., Brosius, A., Drossel, WG., Hintze, W., Ihlenfeldt, S., Nyhuis, P. (eds) Production at the Leading Edge of Technology. WGP 2021. Lecture Notes in Production Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-78424-9_60

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  • DOI: https://doi.org/10.1007/978-3-030-78424-9_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78423-2

  • Online ISBN: 978-3-030-78424-9

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