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Knowledge Graphs (KGs) are increasingly adopted in industrial contexts to support complex decision-making, enhance interoperability, and provide structured semantics for machine learning tasks. They also contribute to the explainablity of industrial AI solutions by enabling inclusion of domain knowledge. This position paper highlights their relevance to tasks such as visual inspection, anomaly detection, predictive maintenance, and root cause analysis. We identify key challenges and propose future research directions to advance the integration of knowledge graphs into industrial AI workflows.

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Toward Explainable Industrial AI: The Role of Knowledge Graphs

  • 会议论文

  • 发表日期：

    2026年

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  • Sabri Manai,
  • Szymon Bobek,
  • Grzegorz J. Nalepa,
  • Luiz do Valle Miranda,
  • Krzysztof Kutt,
  • Jason J. Jung

摘要

Knowledge Graphs (KGs) are increasingly adopted in industrial contexts to support complex decision-making, enhance interoperability, and provide structured semantics for machine learning tasks. They also contribute to the explainablity of industrial AI solutions by enabling inclusion of domain knowledge. This position paper highlights their relevance to tasks such as visual inspection, anomaly detection, predictive maintenance, and root cause analysis. We identify key challenges and propose future research directions to advance the integration of knowledge graphs into industrial AI workflows.