法律关系感知的案件相似度计算方法研究  

Legal Relationship Aware for Computing Legal Case Similarity

作　　者：张冬良 廖永安[1] 程戈[2] Zhang Dongliang;Liao Yongan;Cheng Ge(Law School,Xiangtan University,Xiangtan 411105,China;School of Computer Science&School of Cyberspace Science,Xiangtan University,Xiangtan 411105,China)

机构地区：[1]湘潭大学法学院,湘潭411105 [2]湘潭大学计算机学院·网络空间安全学院,湘潭411105

出　　处：《数据分析与知识发现》2025年第3期136-146,共11页Data Analysis and Knowledge Discovery

基　　金：国家重点研发计划项目(项目编号:2020YFC0832400);湖南省重点研发计划项目(项目编号:2022SK2108)的研究成果之一。

摘　　要：【目的】针对当前案件相似度计算方法存在难以捕捉关键法律要素间长距离、全局和非连续的法律关系,以及文本相似但案件不相似的难分样本区分问题,提出一种更有效的案件相似度计算方法。【方法】构建案件知识图谱结构化表示案件事实,结合图卷积与双向长短期记忆网络编码案件知识图谱,感知主客体间复杂的法律关系,引入难/易混合的负样本挖掘机制提升区分难分样本的能力。【结果】在“中国法研杯”司法人工智能挑战赛提供的基准数据集上的实验表明,所提模型相较冠军模型准确率提升11个百分点,较基于注意力卷积神经网络方法提升7个百分点。【局限】案件知识图谱构建可能会影响相似度计算的效率,但可以通过离线图谱构建、节点预向量化等计算加速策略来克服。【结论】本方法能有效感知关键法律要素间复杂的法律关系,学习不同案件的区别与联系,提升案件相似度计算性能。[Objective]This paper proposes an enhanced case similarity calculation method,addressing the limitations of existing case similarity calculation methods in capturing long-distance,global,and discontinuous legal relationships between key legal elements,as well as the challenges in distinguishing between textually similar but legally dissimilar cases.[Methods]First,we constructed a case knowledge graph to structurally represent factual elements.Then,we combined graph convolutional networks with bidirectional long short-term memory networks to encode the graph and perceive complex legal relationships between subjects and objects.Finally,we introduced a hard/easy mixed negative sample mining mechanism to improve the model’s ability to distinguish difficult cases.[Results]Experiments conducted on the benchmark dataset provided by CAIL show that our proposed model outperforms the champion model by 11%and the optimal attention-based convolutional neural network method by 7%.[Limitations]The construction of the case knowledge graphs may affect the efficiency of similarity computation.However,this issue can be mitigated by strategies such as offline graph construction and node pre-vectorization.[Conclusions]Our method effectively perceives complex legal relationships between key legal elements,learns the distinctions and connections between different cases,and significantly improves the performance of case similarity computation.

关 键 词：知识图谱 图卷积 难分样本挖掘 相似度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]