LLM-M <scp>atcher</scp> : A Name-Based Schema Matching Tool using Large Language Models

We present LLM-Matcher, an interactive name-based schema matching system that utilizes large language models (LLMs) to identify correspondences between source and target schema elements relying solely on their names and descriptions. This tool is specifically designed for restricted environments where instance-based schema matching is not possible, such as the healthcare domain where instance access is often prohibited. LLM-Matcher is based on an extensive experimental study, showing the capabilities of LLMs in the schema matching task. Our system is specifically tailored towards users with sufficient domain knowledge and offers an interpretable initial mapping that can be further refined by providing textual feedback. This feedback allows to rectify model misconceptions as well as improve the quality of schema element descriptions. This paper provides a comprehensive overview of LLM-Matcher, explores its application in the healthcare domain, allows users to gain additional insight into our experimental study, and outlines different steps showcased in the demonstration.

Authors

Institutions

Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1145/3722212.3725112
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LLM-M atcher : A Name-Based Schema Matching Tool using Large Language Models

Frank Neven, Stijn Vansummeren, Brecht Vandevoort, Liesbet M. Peeters et al.
Natural Language Processing Techniques
article

LLM-M atcher : A Name-Based Schema Matching Tool using Large Language Models

Frank Neven, Stijn Vansummeren, Brecht Vandevoort, Liesbet M. Peeters, Marcel Parciak
article en

Abstract

We present LLM-Matcher, an interactive name-based schema matching system that utilizes large language models (LLMs) to identify correspondences between source and target schema elements relying solely on their names and descriptions. This tool is specifically designed for restricted environments where instance-based schema matching is not possible, such as the healthcare domain where instance access is often prohibited. LLM-Matcher is based on an extensive experimental study, showing the capabilities of LLMs in the schema matching task. Our system is specifically tailored towards users with sufficient domain knowledge and offers an interpretable initial mapping that can be further refined by providing textual feedback. This feedback allows to rectify model misconceptions as well as improve the quality of schema element descriptions. This paper provides a comprehensive overview of LLM-Matcher, explores its application in the healthcare domain, allows users to gain additional insight into our experimental study, and outlines different steps showcased in the demonstration.

Hasselt University (BE)
Vlaams Supercomputer Centrum, Fonds Wetenschappelijk Onderzoek, Universiteit Hasselt, Vlaamse regering
Openalex Percentile: Top 93%
Natural Language Processing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

LLM-M <scp>atcher</scp> : A Name-Based Schema Matching Tool using Large Language Models — Frank Neven, Stijn Vansummeren, et al. · (2025) | TGRS Research Map | TGRS