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

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.

Keywords:

• Industrial internet of things
• smart manufacturing
• knowledge graph
• graph embedding
• big data

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author (Lei Ren) upon reasonable request.

Additional information

Funding

The research is supported by The National Key Research and Development Program of China No. 2018YFB1700403, and the NSFC (National Science Foundation of China) project No. 92167108, 62173023, and 61836001.

Notes on contributors

Lei Ren

Lei Ren received the Ph.D. degree in computer science from the Institute of Software, Chinese Academy of Sciences in 2009. Currently, he is a professor at School of Automation Science and Electrical Engineering, Beihang University, China. His research interests include Cloud Manufacturing, Industrial Internet-of-Things, AI and Big Data.

Yingjie Li

Yingjie Li received the Master's degree in Control Engineering from Beihang University, Beijing, China, in 2022. Currently, he is working in China Aviation Industry, Beijing, China. His research interests include Big Data, Industrial Internet-of-Things, and Knowledge Graph.

Xiaokang Wang

Xiaokang Wang received the Ph.D. degree in Computer System Architecture from Huazhong University of Science and Technology, Wuhan, China, in 2017. Currently, he is a Post-Doctoral Fellow with Department of Computer Science, St. Francis Xavier University, Canada. His research interests include Parallel and Distributed Computing, Big Data, Industrial Internet-of-Things, and Cyber-Physical-Social Systems.

Jin Cui

Jin Cui received the B.Eng. degree from Taiyuan University of Technology, Taiyuan, China, in 2013, and the Ph.D. degree from Beihang University, Beijing, China, in 2019. He is currently an Assistant Professor of Research Institute for Frontier Science in Beihang University. He is also with the Ningbo Institute of Technology, Beihang University. His main research interests are in the areas of Intelligent Manufacturing, Digital Twin, Uncertainty Quantification, Prognostic and Health Management.

Lin Zhang

Lin Zhang is a professor of Beihang Unversity. He received the B.S. degree in 1986 from Nankai University, China, the M.S. and the Ph.D. degrees in 1989 and 1992 from Tsinghua University, China. He served as the President of the Society for Modeling and Simulation International (SCS) (2015–2016). He is a Fellow of SCS and ASIASIM. He authored and co-authored 200 papers, 18 books and chapters. His research interests include service oriented Modeling and Simulation, Model Engineering, Cloud Manufacturing and Simulation and their applications in health, etc.