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Industrial Knowledge Graphs (IKGs) play a pivotal role in Industry 4.0, supporting applications like predictive maintenance and production optimization. However, assessing IKG quality is challenging due to spatio-temporal dynamics, multimodal data integration, and noise-induced anomalies. This paper proposes MST-SGAN-KGQA, a framework integrating Multi-modal embeddings, Spatio-Temporal graph neural networks, Generative Adversarial Networks (GANs), and self-supervised learning for Knowledge Graph Quality Assessment. Experimental evaluations demonstrate that MST-SGAN-KGQA outperforms existing methods, showing significant improvements in anomaly detection (0.88) and robustness (0.91), making it suitable for complex industrial environments.

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MST-SGAN-KGQA: An Approach for Industrial Knowledge Graph Quality Assessment

  • 会议论文

  • 发表日期：

    2026年

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  • Xun Zhu,
  • Yuanyuan Li,
  • Linsheng Guo,
  • Bo Huang,
  • Zhijun Fang

摘要

Industrial Knowledge Graphs (IKGs) play a pivotal role in Industry 4.0, supporting applications like predictive maintenance and production optimization. However, assessing IKG quality is challenging due to spatio-temporal dynamics, multimodal data integration, and noise-induced anomalies. This paper proposes MST-SGAN-KGQA, a framework integrating Multi-modal embeddings, Spatio-Temporal graph neural networks, Generative Adversarial Networks (GANs), and self-supervised learning for Knowledge Graph Quality Assessment. Experimental evaluations demonstrate that MST-SGAN-KGQA outperforms existing methods, showing significant improvements in anomaly detection (0.88) and robustness (0.91), making it suitable for complex industrial environments.