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Nowadays, the demand for legal queries is substantial. Therefore, systems must be capable of accurately analyzing the context of queries and retrieving relevant legal regulations. This study introduces a novel intelligent legal query system designed to enhance legal information retrieval. By combining the knowledge base, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs), the system effectively extracts keywords and intricate relationships from legal regulations. RAG is then utilized to retrieve relevant information from extensive databases, while LLMs process and generate comprehensive, human-readable responses. This integrated approach surpasses traditional methods in terms of speed and accuracy, providing a significant advancement in legal information access and utilization.

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Knowledge Graph-Based Legal Query System with LLM and Retrieval Augmented Generation

  • 会议论文

  • 发表日期：

    2025年

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  • Dung V. Dang,
  • Hau Nguyen,
  • Thao T. N. Le,
  • Hung Do,
  • Hung Nguyen,
  • Hung Q. Ngo,
  • Hien D. Nguyen

摘要

Nowadays, the demand for legal queries is substantial. Therefore, systems must be capable of accurately analyzing the context of queries and retrieving relevant legal regulations. This study introduces a novel intelligent legal query system designed to enhance legal information retrieval. By combining the knowledge base, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs), the system effectively extracts keywords and intricate relationships from legal regulations. RAG is then utilized to retrieve relevant information from extensive databases, while LLMs process and generate comprehensive, human-readable responses. This integrated approach surpasses traditional methods in terms of speed and accuracy, providing a significant advancement in legal information access and utilization.