  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.
