A Knowledge Graph Perspective on Supply Chain Resilience
원문 초록
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.
함께 살펴볼 논문
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Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion
Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
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