Text-guided Legal Knowledge Graph Reasoning
This work addresses a domain-specific problem for legal AI applications, focusing on legal provision prediction, and is incremental as it applies a novel method to a known bottleneck in this area.
The paper tackles the problem of predicting related legal provisions for affairs by formulating it as a knowledge graph completion task, and their text-guided graph reasoning approach achieves better performance on the constructed LegalLPP dataset compared to baselines.
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs. We formulate this task as a challenging knowledge graph completion problem, which requires not only text understanding but also graph reasoning. To this end, we propose a novel text-guided graph reasoning approach. We collect amounts of real-world legal provision data from the Guangdong government service website and construct a legal dataset called LegalLPP. Extensive experimental results on the dataset show that our approach achieves better performance compared with baselines. The code and dataset are available in \url{https://github.com/zxlzr/LegalPP} for reproducibility.