LLM-Matcher: A Name-Based Schema Matching Tool using Large Language Models
Summary: LLM-Matcher uses LLMs for name-only schema matching, yielding an interactive, interpretable initial mapping without instance data. Domain-aware feedback refines mappings to correct misconceptions, with experiments validating effectiveness in restricted settings. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Marcel Parciak (Hasselt University)
- 2. Brecht Vandevoort (Hasselt University)
- 3. Frank Neven (Hasselt University)
- 4. Liesbet M. Peeters (Hasselt University)
- 5. Stijn Vansummeren (Hasselt University)
BibTeX Citation
@inproceedings{parciak_sigmod25,
title = {{LLM-Matcher: A Name-Based Schema Matching Tool using Large Language Models}},
author = {Parciak, Marcel and Vandevoort, Brecht and Neven, Frank and Peeters, Liesbet M. and Vansummeren, Stijn},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725112},
url = {https://dl.acm.org/doi/10.1145/3722212.3725112},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 141 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.0002964847 |
| 420 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00018789852 |
| 1,497 | Generic Schema Matching, Ten Years Later | 2011 | VLDB | 0.00010568859 |
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