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Toward Building a Legal Knowledge-Base of Chinese Judicial Documents for Large-Scale Analytics
Amarnath Gupta, Alice Z. Wang, Kai Lin, Haoshen Hong, Haoran Sun, Benjamin L. Liebman, Rachel E. Stern, Subhasis Dasgupta, Margaret E. Roberts
135 - 144
10.3233/978-1-61499-838-9-135
Frontiers in Artificial Intelligence and Applications
Volume 302: Legal Knowledge and Information Systems
Abstract

We present an approach for constructing a legal knowledge-base that is sufficiently scalable to allow for large-scale corpus-level analyses. We do this by creating a polymorphic knowledge representation that includes hybrid ontologies, semistructured representations of sentences, and unsupervised statistical extraction of topics. We apply our approach to over one million judicial decision documents from Henan, China. Our knowledge-base allows us to make corpus-level queries that enable discovery, retrieval, and legal pattern analysis that shed new light on everyday law in China.

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