Towards Scalable Schema Mapping using Large Language Models

Abstract

The growing need to integrate information from many diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex and costly to maintain. While recent advances suggest that large language models (LLMs) can assist in automating schema mapping, key challenges remain. We motivate future research in schema mapping generation by highlighting key challenges, presenting a competitive bidirectional schema matching pipeline, and exploring the limitations of current methods for generating more complex mappings.