[2305.08506] A Knowledge Graph Perspective on Supply Chain Resilience
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Computer Science > Machine Learning

arXiv:2305.08506 (cs)
[Submitted on 15 May 2023]

Title:A Knowledge Graph Perspective on Supply Chain Resilience

Authors:Yushan Liu, Bailan He, Marcel Hildebrandt, Maximilian Buchner, Daniela Inzko, Roger Wernert, Emanuel Weigel, Dagmar Beyer, Martin Berbalk, Volker Tresp
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Abstract:Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to prevent production delays and reduce risks in the supply chains. However, information about supply chains, especially at the deeper levels, is often intransparent and incomplete, making it difficult to obtain precise predictions about prospective risks. By connecting different data sources, we model the supply network as a knowledge graph and achieve transparency up to tier-3 suppliers. To predict missing information in the graph, we apply state-of-the-art knowledge graph completion methods and attain a mean reciprocal rank of 0.4377 with the best model. Further, we apply graph analysis algorithms to identify critical entities in the supply network, supporting supply chain managers in automated risk identification.
Comments: Accepted at the D2R2 workshop (ESWC 2023)
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2305.08506 [cs.LG]
  (or arXiv:2305.08506v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2305.08506

Submission history

From: Yushan Liu [view email]
[v1] Mon, 15 May 2023 10:14:30 UTC (2,163 KB)
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