Automatic Knowledge Graph Construction for Judicial Cases
Jie Zhou Xin Chen Hang Zhang Zhe Li
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.