JurisGraph Insight Engine 1.0v: A Legal Question Answering System Based on Large Language Models and Knowledge Graphs

Haiguang Zhang

Abstract: The extraction and effective utilization of judicial data remains a major challenge in the legal domain. There is a growing mismatch between the public’s demand for accessible legal services and the high cost and complexity of legal consultations, which also affects the efficiency of legal professionals when handling case inquiries. Traditional keyword-based search methods lack professionalism, interpretability, and scalability. In this paper, we propose JurisGraph Insight Engine 1.0v, an intelligent legal question-answering (QA) system that integrates large language models (LLMs) and domain-specific knowledge graphs. We first construct a comprehensive Criminal Law Knowledge Graph (CLKG) containing 483 types of criminal offenses, and develop two unified heterogeneous subgraphs for theft and drug-related cases. Then, we fine-tune a domain-specific legal LLM, LawM, using a curated corpus of over 280,000 Chinese legal records covering multiple legal NLP tasks. Finally, we design and implement a QA system that leverages both the knowledge graph and LawM to deliver accurate and interpretable answers to legal questions. Experimental results show that our system achieves 95\% accuracy, effectively lowering the barrier to legal knowledge access for the general public while improving decision efficiency for legal practitioners.

Submission Number: 66