Measurement of ChatGPT Performance in Mapping Natural Language Speficaction into an Entity Relationship Diagram

This paper explores the entity relationship diagram, a popular conceptual model used to depict entities, attributes, and relationships graphically. To help with this, we use ChatGPT, a sophisticated language model based on the GPT architecture, which can translate natural language text into an entity relationship diagram. The paper details the process of evaluating how well ChatGPT can perform compared to other state-of-the-art approaches for entity and relationship extraction. Our experimental findings demonstrate the strong ability of ChatGPT to translate natural language text into entity relationship diagrams, which has potential applications for knowledge graph building, data integration, and database schema design. Moreover, it can aid in automating the extraction and organization of information from unstructured text data, thereby simplifying the study of complex systems.