# Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms

arXiv: https://arxiv.org/abs/2507.14376 · HTML: https://mlsanity.com/paper/2507.14376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2025.

## Abstract (arXiv)

Schema matching is essential for integrating heterogeneous data sources and enhancing dataset discovery, yet it remains a complex and resource-intensive problem. We introduce SCHEMORA, a schema matching framework that combines large language models with hybrid retrieval techniques in a prompt-based approach, enabling efficient identification of candidate matches without relying on labeled training data or exhaustive pairwise comparisons. By enriching schema metadata and leveraging both vector-based and lexical retrieval, SCHEMORA improves matching accuracy and scalability. Evaluated on the MIMIC-OMOP benchmark, it establishes new state-of-the-art performance, with gains of 7.49% in HitRate@5 and 3.75% in HitRate@3 over previous best results. To our knowledge, this is the first LLM-based schema matching method with an open-source implementation, accompanied by analysis that underscores the critical role of retrieval and provides practical guidance on model selection.