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

现有基于知识图谱的法律判决预测方法重点关注案件的要素实体和关系,不能充分地获取案件的特征信息。针对该问题,提出了一种增强案件特征融合的知识图谱法律判决预测方法。首先,该方法利用双向门控循环神经网络挖掘事实描述文本深层次的因果、时序等全文语义特征信息。然后通过知识图谱向量空间中案例间相似度注意力计算学习类案特征表示。最后,融合特征信息和知识图谱的结构化知识,丰富实体和关系在案件事实文本中的语义特征表示,实现法律判决链路预测任务。在危险驾驶罪和盗窃罪两类罪名数据集上的实验结果显示,该方法在MRR、Hit@1两个关键评价指标上与当前表现最好的链路预测模型相比提升了1.5%左右,Hit@3和Hit@10等指标也均有提升,验证了案件特征增强融合能补充法律知识图谱中缺失的案件特征信息并提高预测的效果。

The existing legal judgment prediction methods based on knowledge graph focus on the element entities and relationships of the case,and cannot adequately capture the characteristic information of the case.Aiming at this problem,the paper 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 attention 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.