[2410.04949] Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law

Computer Science > Information Retrieval arXiv:2410.04949 (cs) [Submitted on 7 Oct 2024 (v1), last revised 3 Mar 2026 (this version, v3)] Title:Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law Authors:Yongming Chen, Miner Chen, Ye Zhu, Juan Pei, Siyu Chen, Yu Zhou, Yi Wang, Yifan Zhou, Hao Li, Songan Zhang View a PDF of the paper titled Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law, by Yongming Chen and 9 other authors View PDF HTML (experimental) Abstract:Judicial efficiency is critical to social stability. However, in many countries worldwide, grassroots courts face substantial case backlogs, and judicial decisions remain heavily dependent on judges' cognitive efforts, with insufficient intelligent tools to enhance efficiency. To address this issue, we propose a highly efficient law article recommendation approach combining a Knowledge Graph (KG) and a Large Language Model (LLM). First, we construct a Case-Enhanced Law Article Knowledge Graph (CLAKG) to store current law articles, historical case information, and their interconnections, alongside an LLM-based automated construction method. Building on this, we propose a closed-loop law article recommendation framework integrating graph embedding-based retrieval and KG-grounded LLM reasoning. Experiments on judgment documents from China Judgments Online demonstrate that our method ...