Integrating AI and Large Language Models for Automated Data Quality Enhancement in Data Integration Systems — Ponon & Basani (2026)

1 More Paper · Sep 23, 2026 · 1h 9m · transcript

This article introduces an AI and LLM-based framework to automate data quality improvement in complex data systems. Traditional methods struggle with semantic inconsistencies and evolving schemas, degrading quality as data scales. The framework incorporates Real-Time Semantic Annotation (RTSA), adaptive ontology reinforcement, contextual similarity for duplicate detection, and continuous auto-healing. Explainability is ensured via SHAP-based alignment for transparency. Evaluated on the GOBY Benchmark dataset, it achieved 89.4% semantic annotation accuracy, outperforming the strongest baseline…