An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis

2025-07-10

AI Summary

Large language models (LLMs) face critical bottlenecks in legal dispute analysis, including weak legal knowledge representation, superficial conceptual understanding, and insufficient logical reasoning. To address these challenges, we propose a novel enhancement framework integrating hierarchical prompt engineering with a multidimensional legal knowledge graph. Our method features a three-stage prompting architecture and a three-tier knowledge graph—comprising ontology, case-law, and statutory layers—augmented with legal reasoning templates, dynamic optimization mechanisms, and four legal concept retrieval strategies. Technically, it incorporates legal ontology modeling, semantic vector retrieval, path-based logical reasoning, and domain-adapted tokenization. Experimental results demonstrate substantial improvements in accuracy for legal applicability analysis in complex cases. The framework exhibits strong generalization capability in judicial decision logic modeling and fine-grained conceptual reasoning. It establishes a new, interpretable, and scalable paradigm for vertical LLM applications in the legal domain.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The rapid development of artificial intelligence has positioned large language models as fundamental components of intelligent legal systems. However, these models face significant limitations in legal dispute analysis, including insufficient legal knowledge representation, limited concept understanding, and reasoning deficiencies. This research proposes an enhanced framework integrating prompt engineering with multidimensional knowledge graphs. The framework introduces a three-stage hierarchical prompt structure comprising task definition, knowledge background, and reasoning guidance, supplemented by legal-specific reasoning templates and dynamic optimization mechanisms. A three-layer knowledge graph architecture is constructed with legal classification ontology, representation, and instance layers. Four complementary methods enable precise legal concept retrieval: direct legal norm code matching, domain-specific semantic vector similarity, ontology-based path reasoning, and specialized lexical segmentation. These components integrate with web search technology to establish a knowledge-enhanced framework for legal decision-making. Experimental results demonstrate significant performance improvements in legal dispute analysis, enabling accurate legal application analysis for complex cases while exhibiting nuanced understanding of judicial decision-making logic, providing a novel technical approach for implementing intelligent legal assistance systems.

Problem

• Enhancing legal knowledge representation in AI models
• Improving concept understanding and reasoning in legal disputes
• Integrating prompt engineering with knowledge graphs for legal analysis

Innovation

• Hierarchical prompt structure enhances legal reasoning
• Three-layer knowledge graph improves legal representation
• Four retrieval methods enable precise concept matching