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.

探索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。利用自然语言处理作为核心技术，自动构建司法案例知识图谱的方法，包括实体识别、关系抽取等多项任务，以及在一个交通事故责任纠纷案例研究中的显著优势。

司法案例的自动知识图谱构建

Automatic Knowledge Graph Construction for Judicial Cases

As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,
cyberattack prevention and data transmission integrity become essential
problems. This study provides a complete framework to handle these difficulties
by integrating a cognitive intelligence (CI) framework, an information
processing protocol, and sophisticated artificial intelligence (AI) and big
data analytics approaches. The CI architecture is intended to improve WSN
security by dynamically reacting to an evolving threat scenario. It employs
artificial intelligence algorithms to continuously monitor and analyze network
behavior, identifying and mitigating any intrusions in real time. Anomaly
detection algorithms are also included in the framework to identify packet drop
instances caused by attacks or network congestion. To support the CI
architecture, an information processing protocol focusing on efficient and
secure data transfer within the WSN is introduced. To protect data integrity
and prevent unwanted access, this protocol includes encryption and
authentication techniques. Furthermore, it enhances the routing process with
the use of AI and big data approaches, providing reliable and timely packet
delivery. Extensive simulations and tests are carried out to assess the
efficiency of the suggested framework. The findings show that it is capable of
detecting and preventing several forms of assaults, including as
denial-of-service (DoS) attacks, node compromise, and data tampering.
Furthermore, the framework is highly resilient to packet drop occurrences,
which improves the WSN's overall reliability and performance

本文提出了一种基于认知智能、信息处理协议、人工智能和大数据分析等方法的框架，旨在提高无线传感器网络（WSN）的安全性和可靠性，该框架可动态地监测和分析网络行为，识别和减轻实时入侵，同时保证数据的完整性和安全传输，增强路由过程的能力，极大地提高了 WSN 的总体效率和性能。