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This paper presents a knowledge graph construction method for legal casedocuments and related laws, aiming to organize legal information efficientlyand enhance various downstream tasks. Our approach consists of three mainsteps: data crawling, information extraction, and knowledge graph deployment.First, the data crawler collects a large corpus of legal case documents andrelated laws from various sources, providing a rich database for furtherprocessing. Next, the information extraction step employs natural languageprocessing techniques to extract entities such as courts, cases, domains, andlaws, as well as their relationships from the unstructured text. Finally, theknowledge graph is deployed, connecting these entities based on their extractedrelationships, creating a heterogeneous graph that effectively represents legalinformation and caters to users such as lawyers, judges, and scholars. Theestablished baseline model leverages unsupervised learning methods, and byincorporating the knowledge graph, it demonstrates the ability to identifyrelevant laws for a given legal case. This approach opens up opportunities forvarious applications in the legal domain, such as legal case analysis, legalrecommendation, and decision support.

Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs