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ReMatch: Retrieval Enhanced Schema Matching with LLMs

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arxiv 2403.01567 v2 pith:F465YUIZ submitted 2024-03-03 cs.DB cs.AI

Eitam Sheetrit , Menachem Brief , Moshik Mishaeli , Oren Elisha This is my paper · ORCID
classification cs.DBcs.AI
keywords schemadatarematchmatchingschemassourcetrainingaccess
verification ladder T0 review T1 audit T2 compute T3 formal
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Schema matching is a crucial task in data integration, involving the alignment of a source schema with a target schema to establish correspondence between their elements. This task is challenging due to textual and semantic heterogeneity, as well as differences in schema sizes. Although machine-learning-based solutions have been explored in numerous studies, they often suffer from low accuracy, require manual mapping of the schemas for model training, or need access to source schema data which might be unavailable due to privacy concerns. In this paper we present a novel method, named ReMatch, for matching schemas using retrieval-enhanced Large Language Models (LLMs). Our method avoids the need for predefined mapping, any model training, or access to data in the source database. Our experimental results on large real-world schemas demonstrate that ReMatch is an effective matcher. By eliminating the requirement for training data, ReMatch becomes a viable solution for real-world scenarios.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLMATCH: A Unified Schema Matching Framework with Large Language Models

    cs.DB 2025-07 conditional novelty 6.0 of 10

    LLMatch adds a Rollup/Drilldown abstraction-refinement strategy to LLM-based schema matching and ships SchemaNet, a multi-table benchmark, reporting higher F1 on complex enterprise schema pairs.

  2. FlowETL: An Autonomous Example-Driven Pipeline for Data Engineering

    cs.SE 2025-07 conditional novelty 5.0 of 10

    FlowETL uses LLMs and a small target dataset to automatically infer and apply data-cleaning transformations, reporting high data-quality scores across 14 datasets.

  3. Towards Scalable Schema Mapping using Large Language Models

    cs.DB 2025-05 conditional novelty 5.0 of 10

    LLM-based schema mapping can be made more scalable and robust through sampled prompts, bidirectional confidence aggregation, and rule chunking, letting a smaller open-source model match GPT-4-based performance on MIMI...

  4. Towards Trustworthy and Cost-Efficient Data Integration: From Na\"ive RAG to Agentic RAG

    cs.DB 2026-07 conditional novelty 4.0 of 10

    The paper argues that agentic RAG with adaptive retrieval, iterative reasoning, and graph memory is the path to trustworthy and cost-efficient data integration, and sketches a six-agent architecture.

  5. Empowering Tabular Data Preparation with Language Models: Why and How?

    cs.AI 2025-08 accept novelty 4.0 of 10

    A structured survey synthesizes LM-based tabular data preparation methods into four phases and two enabling strategies, with qualitative assessments and future directions.

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