RECONSTRUCTING ENTITY RELATIONSHIPS IN DATABASE SCHEMAS WITH PLANTUML AND LLMS

The article explores the potential of using Large Language Models (LLMs) for automatically restoring relationships between tables in SQL databases with incompletely defined foreign keys. To evaluate the ability of LLMs to infer foreign keys from textual descriptions of table structures, an experimental database was created. The database schema, excluding relationships, was provided as input to two large language models: ChatGPT-4o and Claude 3.7 Sonnet. For analysis purposes, only basic information was provided to the LLMs: table names, field names, and primary keys, without any data examples. The ChatGPT-4o model successfully detected all relationships between tables but demonstrated limitations in determining the types of these relationships: all were classified as “one-to-one”, regardless of their actual structure. This indicates the model's inability to accurately interpret the type of relationships based on textual descriptions. In contrast, the Claude 3.7 Sonnet model not only correctly identified all existing relationships, but also correctly determined their types (e.g., one-to-many), demonstrating higher accuracy and a deeper understanding of the database structure within