Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs

TL;DR

This paper introduces a method for constructing a Vietnamese legal knowledge graph using data crawling, NLP-based information extraction, and heterogeneous graph deployment to improve legal information organization and downstream legal tasks.

Contribution

The paper presents a novel approach for building a legal knowledge graph in Vietnamese, integrating heterogeneous data sources and unsupervised learning for legal case analysis.

Findings

The knowledge graph effectively connects legal entities and relationships.

The baseline model can identify relevant laws for legal cases.

Application potential in legal analysis and decision support.

Abstract

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.