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

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.

Automated summary

The Industrial Internet of Things (IIoT) plays a critical role in the development of digital servitization in smart manufacturing. However, there is a knowledge gap between different manufacturing fields, hindering the integration and utilization of industrial big data. To address this challenge, a Framework of Manufacturing Knowledge Graph (FMKG) is proposed, along with an attention-based graph embedding model (ABGE), to extract and complement relationships in the knowledge graph for efficient integration and leverage of industrial knowledge.