In this paper, we explore the application of cognitive intelligence in legal knowledge, focusing on the development of judicial artificial intelligence. Utilizing natural language processing (NLP) as the core technology, we propose a method for the automatic construction of case knowledge graphs for judicial cases. Our approach centers on two fundamental NLP tasks: entity recognition and relationship extraction. We compare two pre-trained models for entity recognition to establish their efficacy. Additionally, we introduce a multi-task semantic relationship extraction model that incorporates translational embedding, leading to a nuanced contextualized case knowledge representation. Specifically, in a case study involving a "Motor Vehicle Traffic Accident Liability Dispute," our approach significantly outperforms the baseline model. The entity recognition F1 score improved by 0.36, while the relationship extraction F1 score increased by 2.37. Building on these results, we detail the automatic construction process of case knowledge graphs for judicial cases, enabling the assembly of knowledge graphs for hundreds of thousands of judgments. This framework provides robust semantic support for applications of judicial AI, including the precise categorization and recommendation of related cases.

翻译：本文探索了认知智能在法律知识中的应用，聚焦于司法人工智能的发展。以自然语言处理（NLP）为核心技术，我们提出了一种面向司法案件的案例知识图谱自动构建方法。该方法围绕两项基础NLP任务展开：实体识别与关系抽取。我们比较了两种用于实体识别的预训练模型以验证其有效性。此外，我们引入了一种融合平移嵌入的多任务语义关系抽取模型，从而实现了语境化的案例知识表示。具体而言，在涉及“机动车交通事故责任纠纷”的案例研究中，我们的方法显著优于基线模型：实体识别的F1值提升了0.36，关系抽取的F1值提升了2.37。基于这些结果，我们详细阐述了司法案件案例知识图谱的自动构建流程，实现了数十万份裁判文书的知识图谱集成。该框架为司法人工智能应用（包括相关案例的精准分类与推荐）提供了稳健的语义支持。