  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.
