计算机应用研究2024，Vol.41Issue(7)：2153-2159,7.DOI:10.19734/j.issn.1001-3695.2023.11.0533
基于知识图谱的案件特征增强法律判决预测
Legal judgment prediction using case feature enhancement based on knowledge graph
摘要
Abstract
The existing legal judgment prediction methods based on knowledge graph focus on the element entities and rela-tionships of the case,and cannot adequately capture the characteristic information of the case.Aiming at this problem,the pa-per proposed a knowledge graph legal judgment prediction method that enhanced the fusion of case features.Firstly,this me-thod used bidirectional gated recurrent neural network to mine the deep semantic feature information such as causality and time sequence of fact description text.Then,it calculated the feature representation of the learning class case by the similarity at-tention between cases in the knowledge graph vector space.Finally,the fusion of feature information and structured knowledge of knowledge graph enriched the semantic feature representation of entities and relationships in the case fact text,and realized the legal judgment link prediction task.The experimental results on the two types of crime datasets of dangerous driving and theft show that the method improves the two key evaluation indicators of MRR and Hit@1 by about 1.5％compared with the current best-performing link prediction models.The indicators such as Hit@3 and Hit@10 are also improved,which verifies that the case feature enhancement fusion can supplement the missing case feature information in the legal knowledge graph and improve the prediction effect.关键词
知识图谱嵌入/特征增强/历史相似案例/法律判决链路预测Key words
knowledge graph embedding/feature enhancement/historical similarity cases/legal judgment link prediction分类
信息技术与安全科学引用本文复制引用
李紫阳,张亚娟,黄义雄,王云鹤..基于知识图谱的案件特征增强法律判决预测[J].计算机应用研究,2024,41(7):2153-2159,7.基金项目
国家青年科学基金资助项目(62206086) （62206086）
天津市教委科研计划资助项目 (2022KJ099) （2022KJ099）
河北省自然科学基金资助项目(F2023202062) （F2023202062）
