Automatic Knowledge Graph Construction for Judicial Cases

TL;DR

This paper presents a novel NLP-based method for automatically constructing judicial case knowledge graphs, significantly improving entity recognition and relationship extraction to support judicial AI applications.

Contribution

It introduces a multi-task semantic relationship extraction model with translational embedding and demonstrates its effectiveness in building large-scale judicial knowledge graphs.

Findings

Entity recognition F1 score increased by 0.36

Relationship extraction F1 score increased by 2.37

Enabled large-scale automatic knowledge graph construction for judicial cases

Abstract

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.