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An ABGE-aided manufacturing knowledge graph construction approach for heterogeneous IIoT data integration

Author

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  • Lei Ren
  • Yingjie Li
  • Xiaokang Wang
  • Jin Cui
  • Lin Zhang

Abstract

The Industrial Internet of Things (IIoT) provides a foundation for the development of emerging digital servitization paradigm in smart manufacturing. The deep integration of massive heterogeneous IIOT data plays a critical role in realising manufacturing digital servitization. However, there is a knowledge gap between different manufacturing fields, which brings a challenge for efficient integration and leverage of industrial big data. For this purpose, a Framework of Manufacturing Knowledge Graph (FMKG) is proposed, which is used to extracts industry knowledge triples from multi-source heterogeneous data to integrate domain knowledge. Also, an attention-based graph embedding model (ABGE) is proposed to discover and complement the implicit missing relationships in the knowledge graph to obtain a complete industrial knowledge graph. The effectiveness of the ABGE model has been verified on several knowledge graph data sets. And an aerospace enterprise production process was taken as an example to establish a product quality knowledge graph, which proved the feasibility of the proposed method.

Suggested Citation

  • Lei Ren & Yingjie Li & Xiaokang Wang & Jin Cui & Lin Zhang, 2023. "An ABGE-aided manufacturing knowledge graph construction approach for heterogeneous IIoT data integration," International Journal of Production Research, Taylor & Francis Journals, vol. 61(12), pages 4102-4116, June.
    • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:12:p:4102-4116
    DOI: 10.1080/00207543.2022.2042416
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