Published In
Proceedings of the 1st Workshop Connecting Academia and Industry on Modern Integrated Database and AI Systems Midas 2025
Document Type
Conference Proceeding
Publication Date
8-19-2025
Subjects
Data Integration -- Computer science, Information storage and retrieval systems
Abstract
The growing need to integrate information from many diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex and costly to maintain. While recent advances suggest that large language models (LLMs) can assist in automating schema mapping, key challenges remain. We motivate future research in schema mapping generation by highlighting key challenges, presenting a competitive bidirectional schema matching pipeline, and exploring the limitations of current methods for generating more complex mappings.
Rights
Copyright (c) 2025 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
DOI
10.1145/3737412.3743490
Persistent Identifier
https://archives.pdx.edu/ds/psu/44213
Citation Details
Buss, C., Safari, M., Termehchy, A., Maier, D., & Lee, S. (2025). Towards Scalable Schema Mapping using Large Language Models. Proceedings of the 1st Workshop Connecting Academia and Industry on Modern Integrated Database and AI Systems, 12–15.