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

arXiv – CS AI|Yongming Chen, Miner Chen, Ye Zhu, Juan Pei, Siyu Chen, Yu Zhou, Yi Wang, Yifan Zhou, Hao Li, Songan Zhang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a new AI system combining Knowledge Graphs and Large Language Models to improve legal article recommendations for Chinese criminal law cases. The system achieved significant accuracy improvements, increasing from 0.549 to 0.694 in recommending relevant law articles for judicial decisions.

Key Takeaways

→A hybrid AI approach combining Knowledge Graphs and LLMs was developed for legal article recommendation in Chinese criminal law.
→The system constructs a Case-Enhanced Law Article Knowledge Graph (CLAKG) to store legal information and case interconnections.
→Experimental results showed accuracy improvements from 0.549 to 0.694 on real judicial documents.
→The research addresses judicial efficiency challenges and case backlogs in grassroots courts.
→All source code and datasets are publicly available on GitHub for reproducibility.

#ai #legal-tech #knowledge-graphs #llm #judicial-efficiency #chinese-law #academic-research #government-ai

Read Original →via arXiv – CS AI

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