Paper ID: 2410.04949 • Published Oct 7, 2024
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
Yongming Chen, Miner Chen, Ye Zhu, Juan Pei, Siyu Chen, Yu Zhou, Yi Wang, Yifan Zhou, Hao Li, Songan Zhang
TL;DR
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Court efficiency is vital for social stability. However, in most countries
around the world, the grassroots courts face case backlogs, with decisions
relying heavily on judicial personnel's cognitive labor, lacking intelligent
tools to improve efficiency. To address this issue, we propose an efficient law
article recommendation approach utilizing a Knowledge Graph (KG) and a Large
Language Model (LLM). Firstly, we propose a Case-Enhanced Law Article Knowledge
Graph (CLAKG) as a database to store current law statutes, historical case
information, and correspondence between law articles and historical cases.
Additionally, we introduce an automated CLAKG construction method based on LLM.
On this basis, we propose a closed-loop law article recommendation method.
Finally, through a series of experiments using judgment documents from the
website "China Judgements Online", we have improved the accuracy of law article
recommendation in cases from 0.549 to 0.694, demonstrating that our proposed
method significantly outperforms baseline approaches.