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Knowledge Graph

Yunbo Long 453 words 3 minutes Knowledge Graph Data Integration Supply Chain Visibility

Knowledge graphs have emerged as a powerful tool for representing and reasoning over the complex, interconnected relationships that define modern supply chains. By structuring entities—such as suppliers, products, facilities, and transactions—as nodes and their relationships as edges, knowledge graphs provide a unified, queryable representation of supply chain ecosystems that supports transparency, risk assessment, and intelligent decision-making.

In supply chain contexts, knowledge graphs address a fundamental challenge: critical information is often fragmented across organisations, systems, and data formats. By integrating heterogeneous data sources into a coherent graph structure, researchers and practitioners can uncover hidden dependencies, identify single points of failure, and trace the provenance of materials across multi-tier networks. This capability is particularly valuable for regulatory compliance, sustainability reporting, and supply chain due diligence.

Recent research—including contributions from the Supply Chain AI Lab at the University of Cambridge—has explored the construction of supply chain knowledge graphs from diverse data sources, the application of graph reasoning for risk propagation analysis, and the integration of knowledge graphs with machine learning for predictive supply chain analytics.

We invite you to explore the curated collection of key publications below, offering a gateway into this rapidly evolving field.

List of Publications

  1. Brintrup, A., Ledwoch, A. and Barber, J., 2018. Connected factories challenge: Supply network discovery and mapping. EPSRC Network Plus Report. [PDF]
  2. Kosasih, E.E. and Brintrup, A., 2022. A machine learning approach for predicting hidden links in supply chain with graph neural networks. International Journal of Production Research, 60(17), pp.5380-5393. [PDF]
  3. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G.D., Gutierrez, C., Kirrane, S., Gayo, J.E.L., Navigli, R., Neumaier, S. and Ngomo, A.C.N., 2021. Knowledge graphs. ACM Computing Surveys, 54(4), pp.1-37. [PDF]
  4. Heijmans, S., van Dinter, R. and Brintrup, A., 2023. Knowledge graph construction for supply chain management: A review. Computers in Industry, 150, p.103938. [PDF]
  5. Li, M., Brintrup, A. and Tiwari, A., 2020. A knowledge graph-based approach for exploring supply chain research trends. International Journal of Production Economics, 228, p.107729. [PDF]
  6. Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S. and Zhang, W., 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.601-610. [PDF]