用大模型构建塞内加尔法律知识图谱，提升法律文本可读性。
An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph
- 用大模型从法律文本中提取实体与关系，构建知识图谱。
- 成功提取7967条法律条文，构建含2872节点、10774关系的图数据库。
- 适用于法律从业者与普通民众，助力理解塞内加尔法律权利。
本研究探讨人工智能与大语言模型（LLM）在改善塞内加尔司法体系中法律文本可及性方面的应用。针对法律文件提取与组织困难的问题，研究从各类法律文献中成功提取了7,967条条文，重点聚焦《土地与公共财产法典》。构建了一个包含2,872个节点和10,774条关系的详细图数据库，有助于可视化法律条文间的关联。采用先进的三元组抽取技术，验证了GPT-4o、GPT-4和Mistral-Large等模型在识别法律关系与元数据方面的有效性。通过这些技术，旨在建立一个坚实框架，使塞内加尔公民与法律专业人士能更高效地理解自身权利与义务。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。