Schema Matching

Schema matching aims to automatically identify corresponding elements across different databases or data schemas, a crucial step in data integration and interoperability. Current research heavily utilizes large language models (LLMs) and other deep learning techniques to improve matching accuracy, often incorporating strategies like prompt engineering, retrieval augmentation, and generative tagging to handle semantic heterogeneity and limited data. These advancements are significantly impacting data management and analysis by streamlining data integration processes, enabling more efficient data warehousing, and facilitating cross-database analyses in fields like healthcare and humanitarian aid. The development of novel benchmark datasets and algorithms focused on minimizing data exposure for privacy reasons also represents a key trend.