  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/zjunlp/LegalPP} for reproducibility.
