利用生成式人工智能增强供应链可见性：知识图谱中关系预测的探索性案例研究
Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs
AI总结：
本文提出结合预训练语言模型嵌入与机器学习的 GenAI 框架，用于知识图谱供应链关系预测，并通过真实案例证明其优于现有基准。
摘要：
企业和政策制定者有效管理供应链风险的一个主要障碍，是缺乏对相互依赖的供应网络关系的可见性。关系预测（也称为链路预测）是供应链监测研究中的一个新兴领域，旨在利用数据驱动技术提高供应链可见性。现有方法在预测关系方面已经取得成功，但难以提取这些关系所嵌入的语境，例如所供应的产品或供应来源地。语境缺失使从业者无法区分交易关系与已建立的供应链关系，从而阻碍对风险的准确估计。在本研究中，我们开发了一种新的生成式人工智能（Gen AI）增强型机器学习框架，该框架利用预训练语言模型作为嵌入模型，并将其与机器学习模型相结合，以预测知识图谱中的供应链关系。通过整合生成式人工智能技术，我们的方法能够捕捉实体之间细微的语义关系，从而提高供应链可见性并促进更精确的风险管理。利用真实案例研究的数据，我们表明 GenAI 增强的链路预测优于所有基准，并展示了如何在供应链风险管理中探索和有效使用 GenAI 模型。
英文摘要：
A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of supply chains using data-driven techniques. Existing methods have been successful for predicting relationships but struggle to extract the context in which these relationships are embedded - such as the products being supplied or locations they are supplied from. Lack of context prevents practitioners from distinguishing transactional relations from established supply chain relations, hindering accurate estimations of risk. In this work, we develop a new Generative Artificial Intelligence (Gen AI) enhanced machine learning framework that leverages pre-trained language models as embedding models combined with machine learning models to predict supply chain relationships within knowledge graphs. By integrating Generative AI techniques, our approach captures the nuanced semantic relationships between entities, thereby improving supply chain visibility and facilitating more precise risk management. Using data from a real case study, we show that GenAI-enhanced link prediction surpasses all benchmarks, and demonstrate how GenAI models can be explored and effectively used in supply chain risk management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
