Towards trustworthy AI for link prediction in supply chain knowledge graph: a neurosymbolic reasoning approach
针对供应链监控中链接预测的黑箱问题，基于神经符号AI设计了一种更透明的机器学习方法，在汽车和能源数据集上性能与现有黑箱模型相当，同时提升了可解释性和复杂度分析。
Modern supply chains are complex and interlinked, resulting in increased network risk exposure for companies. Digital Supply Chain Surveillance (DSCS) has emerged as a practice to proactively monitor these risks. Within DSCS, link prediction, a particular task whose objective is to identify hidden relationships in the supply chain, has been increasingly studied in the literature. While many approaches have been proposed, machine learning (ML) based techniques have recently gained wider attention due to their state-of-the-art performance across different benchmark datasets. However, adoption of these technologies in practice remains difficult. Their black-box nature have resulted in a lack of trustworthiness among practitioners. In this paper, we design a trustworthy ML approach based on recent theoretical development in neurosymbolic AI methods that enables a more transparent learning and reasoning process. We find that our approach is not only on par with state-of-the-art black box models when tested in two benchmark datasets on the automotive and energy industry but also addresses research gaps on explainability and complexity analysis.