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arXiv · 2507.14376

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

Osman Erman Gungor·Derak Paulsen·William Kang
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Abstract

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.

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Osman Erman Gungor, Derak Paulsen, William Kang. 2025-07-18. Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms. https://arxiv.org/abs/2507.14376

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