基于多视角知识图谱嵌入的量刑预测

Sentencing Prediction Based on Multi-view Knowledge Graph Embedding

量刑预测是智慧司法建设的重要组成部分.为了解决量刑预测的可解释性问题,文中将量刑预测任务重新定义为基于知识图谱的链路预测任务,提出多视角的知识图谱嵌入方法,预测案件量刑.首先,文中设计知识图谱本体模式,用于指导案情描述中关键要素的提取.然后,使用图嵌入技术,从案件要素构成的辅助图中学习要素的初始表示.最后,融合知识图谱的结构化特征,对案件要素进行增强表示.以贩卖毒品类案件为研究对象,文中方法在基于知识图谱的量刑预测任务中性能较优,量刑结果有较好的可解释性.

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.