Snorkel AI Method for Supply Chain Event Extraction and Risk Assessment

The utilization of Snorkel AI has gained popularity in data-centric computing due to its inherent advantages of quick and systematic iterative processes, along with maximum data utility capability. One of the major challenges in artificial intelligence is the labeling from event extraction. However, a Snorkel AI-based model has been proposed to address this challenge for event extraction. This model aims to improve the accuracy and efficiency of the labeling process by leveraging supervision techniques. Furthermore, the effectiveness of the risk assessment system depends on the quality and relevance of the labeled data. We perform risk assessment through the K-Nearest Neighbor (KNN) Algorithm. The model achieves an accuracy of 92.4% and significantly improves the precision of supply chain risk assessments.