Document Type
Article
Abstract
Understanding causal relations within manufacturing pipelines is crucial for key manufacturing tasks such as anomaly detection and root cause analysis. However, existing causal machine learning (causal ML) approaches struggle to scale effectively to the vast number of variables present in manufacturing settings. We advocate for incorporating domain knowledge within the manufacturing pipelines, represented as knowledge graphs (KGs), for designing causal ML methods for large-scale manufacturing problems. Knowledge graphs can encode rich contextual information about the interactions and dependencies between different components and stages of the manufacturing pipeline, providing a structured framework to guide the discovery of causal relationships. By incorporating KGs, causal ML models can leverage both data-driven approaches and domain knowledge, enhancing scalability and improving the accuracy of causal learning in large scale manufacturing settings.
Publication Info
Preprint version 2024.
Rights
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
APA Citation
Zi, Y., Henson, C., & Sheth, A. (2024). Empowering Causal Machine Learning for Large-scale Manufacturing Pipelines with Knowledge Graphs. The 23rd International Semantic Web Conference.