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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1466))

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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 for reproducibility.

L. Li, Z. Bi and H. Ye—Equal contribution and shared co-first authorship.

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Correspondence to Shumin Deng .

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Li, L., Bi, Z., Ye, H., Deng, S., Chen, H., Tou, H. (2021). Text-Guided Legal Knowledge Graph Reasoning. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore.

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