Contract review is a critical yet time-consuming process in legal practice, with significant financial implications when errors occur. While Large Language Models (LLMs) have shown promise in legal document processing, they still face challenges with lengthy contracts and complex legal relationships. This research presents an advanced approach to automated contract review by integrating knowledge graphs into Retrieval-Augmented Generation (RAG) frameworks, addressing the limitations of current methodologies.

Through a comprehensive literature review of contract review automation and RAG systems, we conducted systematic experiments comparing RAG approaches with LLMs' in-context learning capabilities. Our empirical analysis validated that RAG-based methods significantly enhance long-context text analysis and information extraction in legal documents, particularly in terms of accuracy and consistency.

Building on these findings, we extensively investigated optimization techniques for the RAG retrieval phase, recognizing its critical role in contract review accuracy. Our experimental evaluation encompassed various chunking strategies, query expansion methods, and re-ranking approaches, establishing best practices for legal document processing.

Our primary contribution is a novel KG-RAG system that enhances contextual understanding in legal document analysis. We evaluate our approach using the Contract Understanding Atticus Dataset (CUAD) and ContractNLI dataset, demonstrating improved performance over traditional RAG implementations and long-context models. The research also explores optimal chunking strategies and investigates the efficiency-effectiveness trade-offs between different model architectures.

Results indicate that our KG-enhanced RAG framework achieves superior performance in identifying and analyzing complex legal relationships while maintaining computational efficiency. The integration of knowledge graphs particularly excels in capturing hierarchical and cross-referential relationships within legal documents, a crucial aspect often overlooked by conventional approaches.

This work advances the field of legal AI by providing a more robust and context-aware approach to contract review, while offering practical insights for implementing AI systems in legal practice. Our findings suggest promising directions for future research in legal document processing, particularly in areas requiring deep contextual understanding and relationship modeling.