Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908598257319936 |
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| author | Wang, Qin Li, Youhuan Feng, Yansong Chen, Si Li, Ziming Zhang, Pan Si, Zihui Chen, Yixuan Shi, Zhichao Huang, Zebin Chen, Guo Jin, Wenqiang |
| author_facet | Wang, Qin Li, Youhuan Feng, Yansong Chen, Si Li, Ziming Zhang, Pan Si, Zihui Chen, Yixuan Shi, Zhichao Huang, Zebin Chen, Guo Jin, Wenqiang |
| contents | People without a database background usually rely on file systems or tools such as Excel for data management, which often lead to redundancy and data inconsistency. Relational databases possess strong data management capabilities, but require a high level of professional expertise from users. Although there are already many works on Text2SQL to automate the translation of natural language into SQL queries for data manipulation, all of them presuppose that the database schema is pre-designed. In practice, schema design itself demands domain expertise, and research on directly generating schemas from textual requirements remains unexplored. In this paper, we systematically define a new problem, called Text2Schema, to convert a natural language text requirement into a relational database schema. With an effective Text2Schema technique, users can effortlessly create database table structures using natural language, and subsequently leverage existing Text2SQL techniques to perform data manipulations, which significantly narrows the gap between non-technical personnel and highly efficient, versatile relational database systems. We propose SchemaAgent, an LLM-based multi-agent framework for Text2Schema. We emulate the workflow of manual schema design by assigning specialized roles to agents and enabling effective collaboration to refine their respective subtasks. We also incorporate dedicated roles for reflection and inspection, along with an innovative error detection and correction mechanism to identify and rectify issues across various phases. Moreover, we build and open source a benchmark containing 381 pairs of requirement description and schema. Experimental results demonstrate the superiority of our approach over comparative work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language Wang, Qin Li, Youhuan Feng, Yansong Chen, Si Li, Ziming Zhang, Pan Si, Zihui Chen, Yixuan Shi, Zhichao Huang, Zebin Chen, Guo Jin, Wenqiang Databases Artificial Intelligence People without a database background usually rely on file systems or tools such as Excel for data management, which often lead to redundancy and data inconsistency. Relational databases possess strong data management capabilities, but require a high level of professional expertise from users. Although there are already many works on Text2SQL to automate the translation of natural language into SQL queries for data manipulation, all of them presuppose that the database schema is pre-designed. In practice, schema design itself demands domain expertise, and research on directly generating schemas from textual requirements remains unexplored. In this paper, we systematically define a new problem, called Text2Schema, to convert a natural language text requirement into a relational database schema. With an effective Text2Schema technique, users can effortlessly create database table structures using natural language, and subsequently leverage existing Text2SQL techniques to perform data manipulations, which significantly narrows the gap between non-technical personnel and highly efficient, versatile relational database systems. We propose SchemaAgent, an LLM-based multi-agent framework for Text2Schema. We emulate the workflow of manual schema design by assigning specialized roles to agents and enabling effective collaboration to refine their respective subtasks. We also incorporate dedicated roles for reflection and inspection, along with an innovative error detection and correction mechanism to identify and rectify issues across various phases. Moreover, we build and open source a benchmark containing 381 pairs of requirement description and schema. Experimental results demonstrate the superiority of our approach over comparative work. |
| title | Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2503.23886 |