This paper presents a knowledge graph construction method for legal case documents and related laws, aiming to organize legal information efficiently and enhance various downstream tasks. Our approach consists of three main steps: data crawling, information extraction, and knowledge graph deployment. First, the data crawler collects a large corpus of legal case documents and related laws from various sources, providing a rich database for further processing. Next, the information extraction step employs natural language processing techniques to extract entities such as courts, cases, domains, and laws, as well as their relationships from the unstructured text. Finally, the knowledge graph is deployed, connecting these entities based on their extracted relationships, creating a heterogeneous graph that effectively represents legal information and caters to users such as lawyers, judges, and scholars. The established baseline model leverages unsupervised learning methods, and by incorporating the knowledge graph, it demonstrates the ability to identify relevant laws for a given legal case. This approach opens up opportunities for various applications in the legal domain, such as legal case analysis, legal recommendation, and decision support.

翻译：本文提出了一种面向法律案例文档及相关法律法规的知识图谱构建方法，旨在高效组织法律信息并增强各类下游任务。该方法包含三个核心步骤：数据爬取、信息抽取与知识图谱部署。首先，数据爬取器从多源渠道收集大量法律案例文档及相关法律法规，为后续处理提供丰富数据库。其次，信息抽取步骤利用自然语言处理技术，从非结构化文本中提取法院、案例、领域、法律等实体及其关系。最终，知识图谱部署环节基于已抽取的关系连接这些实体，构建能有效表征法律信息的异构图，并服务于律师、法官、学者等用户。所建立的基线模型采用无监督学习方法，通过融合知识图谱，展现出为特定法律案例检索相关法规的能力。该研究为法律案例分析、法律推荐及决策支持等法律领域应用开辟了新可能。