# Constructing a Knowledge Graph for Vietnamese Legal Cases with Heterogeneous Graphs

arXiv: https://arxiv.org/abs/2309.09069 · HTML: https://mlsanity.com/paper/2309.09069/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 16 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)
- [Encoded Summarization: Summarizing Documents into Continuous Vector Space for Legal Case Retrieval](https://mlsanity.com/paper/2309.08187/) (2023)

## Built on by (most cited first, 2 of 2)

- [Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned](https://mlsanity.com/paper/2508.06368/) (2025)
- [An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph](https://mlsanity.com/paper/2510.02353/) (2025)

## Abstract (arXiv)

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.