用知识图谱和大模型构建意大利立法辅助平台，提升法律分析与制定效率。
Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems
- 融合立法知识图谱与大语言模型，构建可交互的立法分析系统。
- 实现法律条文间关联可视化，支持精准检索与上下文理解。
- 面向非技术用户设计界面，助力立法人员高效开展工作。
知识图谱（KGs）可将大规模数据组织为结构化、互联的信息，提升各领域的数据分析能力。在立法领域，知识图谱自然适用于建模法律条文之间及其与更广泛立法背景的复杂关联。同时，GPT等大语言模型（LLMs）在文本生成与文件起草方面展现出新机遇。然而，其在立法场景中的应用至关重要，需避免幻觉并依赖最新信息，因新法每日发布。本文研究立法知识图谱与大语言模型如何协同支持立法流程，聚焦三个问题：知识图谱对立法系统的益处；大模型如何确保输出准确以支持立法活动；以及如何让非技术人员使用该技术。为此，我们开发了面向意大利立法的Legis AI平台，旨在增强立法分析能力，并支持立法工作。
原文摘要 · Abstract (English)
Knowledge Graphs (KGs) have been used to organize large datasets into structured, interconnected information, enhancing data analytics across various fields. In the legislative context, one potential natural application of KGs is modeling the intricate set of interconnections that link laws and their articles with each other and the broader legislative context. At the same time, the rise of large language models (LLMs) such as GPT has opened new opportunities in legal applications, such as text generation and document drafting. Despite their potential, the use of LLMs in legislative contexts is critical since it requires the absence of hallucinations and reliance on up-to-date information, as new laws are published on a daily basis. This work investigates how Legislative Knowledge Graphs and LLMs can synergize and support legislative processes. We address three key questions: the benefits of using KGs for legislative systems, how LLM can support legislative activities by ensuring an accurate output, and how we can allow non-technical users to use such technologies in their activities. To this aim, we develop Legis AI Platform, an interactive platform focused on Italian legislation that enhances the possibility of conducting legislative analysis and that aims to support lawmaking activities.
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