ENTITY-RELATIONSHIP DIAGRAM GENERATION WITH NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING APPROACH

As software systems continue to grow in complexity, the need for efficient and accurate design methodologies becomes increasingly critical. Entity-Relationship Diagrams (ERDs) provide a powerful visual representation of system structures and dependencies, serving as a foundation for software engineering and database design. However, manually creating ERDs from textual requirements is time-consuming and manual. To address this challenge, this research explores the application of natural language processing (NLP) techniques to automatically extract relevant information from unstructured text and generate ERDs. The proposed approach leverages the strengths of rule-based techniques, semantic analysis, and machine learning algorithms to automatically identify entities, attributes, relationships, and cardinalities from natural language input. Our study offers practical insights into the utilization of linguistic and semantic analysis, and machine learning for efficient information extraction. The proposed system aims to streamline the ERD creation process and improve the accuracy and quality of the resulting diagrams. While the proposed approach shows promising results, the limitations in heuristic rule coverage and data dependencies are acknowledge. Furthermore, the evaluation results demonstrate in detecting entities, attributes, and relations, with f1-scores of 0.96, 0.93, and 0.92, and resolving the components specifications achieved accuracy of 0.87, 0.84, 0.91, respectively. The findings contribute to advancing ERD extraction from text and suggest future research directions for improving the robustness and usability of the solution. The fusion of NLP techniques with ERD creation highlights the potential for enhancing the software development lifecycle and opens new avenues for research in the realm of information extraction from natural language text. Keywords: Entity-Relationship Diagram, Natural Language Processing, Named Entity Recognition, Information Extraction.