Text-guided Legal Knowledge Graph Reasoning

TL;DR

This paper introduces a novel text-guided reasoning method for legal knowledge graphs, specifically predicting legal provisions from text, and demonstrates its effectiveness on a newly constructed real-world dataset.

Contribution

It proposes a new text-guided graph reasoning approach for legal provision prediction and provides a large, real-world dataset called LegalLPP.

Findings

Our approach outperforms baseline methods on the LegalLPP dataset.

The dataset LegalLPP is a valuable resource for legal knowledge graph research.

The method effectively combines text understanding with graph reasoning.

Abstract

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.