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.
Fund:National Key Research and Development Program of China(No. 2018YFC0830603)
Corresponding Authors:
SUN Yuanyuan, Ph.D., professor. Her research interests include natural language processing, nonlinear theory and applications, and machine learning.
About author:: WANG Zhizheng, Ph.D.candidate. His research interests include representation lear-ning and knowledge graph reasoning.WANG Lei, Ph.D. His research interests include criminal judicature.LI Shuaichi, master student. His research interests include knowledge graph based question answering.CHEN Yanguang, master student. Her research interests include text mining and know-ledge graph construction.XU Ce, master student. His research inte-rests include text automatic summarization and legal artificial intelligence.WANG Gang, master student. His research interests include text summarization.LIN Hongfei, Ph.D., professor. His research interests include sentiment analysis and opinion mining, information retrieval and re-commendation, and knowledge mining.
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2021, Vol. 34 
