ScholarWorks@중앙대학교

초록

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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