An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph
This work addresses the need for better access to judicial information for Senegalese citizens and legal professionals, though it is incremental as it applies existing methods to a new domain-specific dataset.
The study tackled the problem of extracting and organizing legal texts in Senegal's judicial system by applying AI and large language models, resulting in the extraction of 7,967 articles and the creation of a graph database with 2,872 nodes and 10,774 relationships to visualize interconnections.
This study examines the application of artificial intelligence (AI) and large language models (LLM) to improve access to legal texts in Senegal's judicial system. The emphasis is on the difficulties of extracting and organizing legal documents, highlighting the need for better access to judicial information. The research successfully extracted 7,967 articles from various legal documents, particularly focusing on the Land and Public Domain Code. A detailed graph database was developed, which contains 2,872 nodes and 10,774 relationships, aiding in the visualization of interconnections within legal texts. In addition, advanced triple extraction techniques were utilized for knowledge, demonstrating the effectiveness of models such as GPT-4o, GPT-4, and Mistral-Large in identifying relationships and relevant metadata. Through these technologies, the aim is to create a solid framework that allows Senegalese citizens and legal professionals to more effectively understand their rights and responsibilities.