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

• URL: http://arxiv.org/abs/2507.14376v1
• Date: Fri, 18 Jul 2025 21:50:36 GMT
• Title: Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
• Authors: Osman Erman Gungor, Derak Paulsen, William Kang,
• Abstract summary: SCHEMORA is a schema matching framework that combines large language models with hybrid retrieval techniques.<n>It is evaluated on the MIMIC-OMOP benchmark, with gains of 7.49% in HitRate@5 and 3.75% in HitRate@3 over previous best results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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