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The emergence of smart manufacturing within the framework of Industry 4.0, has posed major challenges especially towards the management of numerous data sources, which includes, but not limited to business systems, machines, and sensors. The limitations presented by traditional approaches to such situations creates lags on the ability to make timely decisions and integrate different systems. The outcome of this study is a proposal of a method that will use knowledge graph technique in order to organize various kinds of industrial data into ontological and contextual forms. This includes ontology engineering, integration of semantic data, and database design, monitoring solutions in real time and investigating the sources of problems. The approach demonstrates how a knowledge graph manages heterogeneous data using the example of simulation based case study. This led to great enhancements for the obtained model in terms of data integration, query performance, relationships and visualizations, and cognitive efficiency, meeting the sophisticated needs of industry 5.0.

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Enhancing Data Integration and Decision-Making in Smart Manufacturing Through Knowledge Graph-Based Management of Heterogeneous Data

  • 会议论文

  • 发表日期：

    2026年

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  • Rania Hamdani,
  • Joma Aldrini,
  • Inès Chihi

摘要

The emergence of smart manufacturing within the framework of Industry 4.0, has posed major challenges especially towards the management of numerous data sources, which includes, but not limited to business systems, machines, and sensors. The limitations presented by traditional approaches to such situations creates lags on the ability to make timely decisions and integrate different systems. The outcome of this study is a proposal of a method that will use knowledge graph technique in order to organize various kinds of industrial data into ontological and contextual forms. This includes ontology engineering, integration of semantic data, and database design, monitoring solutions in real time and investigating the sources of problems. The approach demonstrates how a knowledge graph manages heterogeneous data using the example of simulation based case study. This led to great enhancements for the obtained model in terms of data integration, query performance, relationships and visualizations, and cognitive efficiency, meeting the sophisticated needs of industry 5.0.