Natural Language to SQL: A Semantic Mapping and Metadata Approach for Database Interaction
- DOI
- 10.2991/978-94-6463-858-5_117How to use a DOI?
- Keywords
- Natural language interface; SQL; natural language processing; relational database; metadata; NLIDB; Query; semantic mapping
- Abstract
Databases play a vital role in organizing and retrieving the information we rely on every day. The evolution of computing has elevated the importance of databases in efficiently storing and managing information. Typically, users need to understand SQL—a query language—to fetch data from these systems. But for those unfamiliar with SQL syntax or the database structure, writing queries can be a challenge. This has led to the idea of enabling database access through natural language instead of SQL queries. This paper explores the development of an approach for converting Natural Language question into SQL query. The proposed method leverages semantic mapping and metadata to interpret natural language and convert it into SQL queries. The primary goal is to interpret user queries expressed in natural language, convert them into SQL statements, and fetch the corresponding data from the database by running those SQL queries.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jaina Patel PY - 2025 DA - 2025/11/04 TI - Natural Language to SQL: A Semantic Mapping and Metadata Approach for Database Interaction BT - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025) PB - Atlantis Press SP - 1405 EP - 1418 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-858-5_117 DO - 10.2991/978-94-6463-858-5_117 ID - Patel2025 ER -