GNN-ERE: A Graph Neural Network Framework for Entity-Relation Extraction and Intelligent Legal Case Reasoning
Abstract
The construction of legal knowledge graphs and intelligent case reasoning systems is pivotal for enhancing legal information retrieval, case analysis, and judicial decision-making. This research focuses on utilizing graph neural networks (GNNs) to model complex legal entities and their interrelations for structured legal knowledge representation. Traditional methods often rely on keyword matching and shallow semantic analysis, which struggle to capture deep legal logic, implicit correlations, and multi-level entity relations, leading to low reasoning accuracy and limited adaptability. To address these challenges, this paper proposes a novel Graph Neural Network with Entity-Relation Extraction (GNN-ERE) framework. It automatically constructs a legal knowledge graph by extracting entities and relationships from legal texts and integrates it with graph-based reasoning to support intelligent case analysis and precedent retrieval. The proposed method enables automated legal case reasoning by identifying similar legal cases based on contextual and structural similarities within the knowledge graph, thereby supporting judges and legal practitioners in evidence-based decision-making. Experimental results demonstrate that the GNN-ERE framework significantly improves the accuracy of legal case similarity matching and reasoning, enhances the interpretability of the system, and provides a scalable solution for legal AI applications. The proposed method gradually improves the entity extraction accuracy by 89.3%, relation extraction precision by 96%, graph construction time by 9.8 ms, case similarity score by 95%, classification accuracy by 94%, and reasoning time efficiency by 298 ms.References
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