Efficient Knowledge Graph Construction and Retrieval from Unstructured Text for Large-Scale RAG Systems

• URL: http://arxiv.org/abs/2507.03226v1
• Date: Fri, 04 Jul 2025 00:05:55 GMT
• Title: Efficient Knowledge Graph Construction and Retrieval from Unstructured Text for Large-Scale RAG Systems
• Authors: Congmin Min, Rhea Mathew, Joyce Pan, Sahil Bansal, Abbas Keshavarzi, Amar Viswanathan Kannan,
• Abstract summary: We propose a scalable and cost-efficient framework for deploying Graph-based Retrieval Augmented Generation (GraphRAG) in enterprise environments.<n>These results validate the feasibility of deploying GraphRAG systems in real-world, large-scale enterprise applications.
• Score: 0.06597195879147556
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a scalable and cost-efficient framework for deploying Graph-based Retrieval Augmented Generation (GraphRAG) in enterprise environments. While GraphRAG has shown promise for multi-hop reasoning and structured retrieval, its adoption has been limited by the high computational cost of constructing knowledge graphs using large language models (LLMs) and the latency of graph-based retrieval. To address these challenges, we introduce two core innovations: (1) a dependency-based knowledge graph construction pipeline that leverages industrial-grade NLP libraries to extract entities and relations from unstructured text completely eliminating reliance on LLMs; and (2) a lightweight graph retrieval strategy that combines hybrid query node identification with efficient one-hop traversal for high-recall, low-latency subgraph extraction. We evaluate our framework on two SAP datasets focused on legacy code migration and demonstrate strong empirical performance. Our system achieves up to 15% and 4.35% improvements over traditional RAG baselines based on LLM-as-Judge and RAGAS metrics, respectively. Moreover, our dependency-based construction approach attains 94% of the performance of LLM-generated knowledge graphs (61.87% vs. 65.83%) while significantly reducing cost and improving scalability. These results validate the feasibility of deploying GraphRAG systems in real-world, large-scale enterprise applications without incurring prohibitive resource requirements paving the way for practical, explainable, and domain-adaptable retrieval-augmented reasoning.

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