Sentencing Prediction Based onMulti-view Knowledge Graph Embedding
WANG Zhizheng1, WANG Lei2, LI Shuaichi1, SUN Yuanyuan1, CHEN Yanguang1, XU Ce1, WANG Gang1, LIN Hongfei1
1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024; 2. People′s Procuratorate of Jinzhou City, Liaoning Province, Jinzhou 111000
Abstract：Sentencing prediction is a crucial component of smart judicial construction. To make sentencing results more interpretable, the sentencing prediction task is defined as a link prediction task based on a knowledge graph. In this paper, a multi-view knowledge graph embedding method is proposed to predict the sentencing of a case. Firstly, a knowledge graph ontology pattern is designed to guide the extraction of essential elements in the case description. Next, an auxiliary graph is constructed by the extracted elements and the graph embedding method is applied to learn the initial representations of elements from this auxiliary graph. Finally, the representation of elements is enhanced by fusing the structural features of the knowledge graph. Taking drug trafficking cases as the research data, the proposed method generates better performance in sentencing prediction task based on knowledge graph, and the interpretability of sentencing results is improved.
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2021, Vol. 34 
