Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scale

A scalable GraphRAG framework for enterprise deployment that replaces expensive LLM-based knowledge graph construction with dependency parsing and combines vector search with graph traversal via Reciprocal Rank Fusion, achieving near-LLM performance at significantly lower cost.

Problem Statement

GraphRAG offers strong multi-hop reasoning capabilities but remains impractical in production due to high costs from LLM-based entity/relation extraction and complex graph traversal. Existing approaches lack scalability and cost-efficiency for enterprise deployments. This work addresses the gap between research promise and real-world feasibility in domain-specific retrieval tasks.

Key Novelty

Dependency parsing-based knowledge graph construction pipeline that achieves 94% of LLM-based extraction performance (61.87% vs 65.83%) at dramatically reduced cost and improved scalability
Hybrid retrieval strategy fusing vector similarity with graph traversal using Reciprocal Rank Fusion (RRF) across separate embeddings for entities, chunks, and relations enabling multi-granular matching
Practical enterprise validation on legacy code migration datasets showing up to 15% improvement over vanilla vector retrieval baselines using LLM-as-Judge evaluation

Evaluation Highlights

Dependency parsing KG construction achieves 61.87% vs LLM-based 65.83% (94% relative performance) on extraction quality benchmarks
Hybrid retrieval improves up to 15% and 4.35% over vanilla vector retrieval baselines on two enterprise legacy code migration datasets using LLM-as-Judge metrics

Breakthrough Assessment

5/10 The paper makes a solid engineering contribution by demonstrating that classical NLP techniques can substitute LLM-based KG construction at scale, and the hybrid RRF fusion is practically impactful; however, the core ideas are incremental combinations of existing techniques rather than fundamentally new algorithms.

Methodology

Construct knowledge graphs using dependency parsing to extract entities and relations from enterprise documents, replacing costly LLM-based NLP pipelines while retaining ~94% extraction quality
Generate and maintain separate vector embeddings for entities, document chunks, and relations to support multi-granular retrieval across different semantic levels of the knowledge graph
At query time, fuse graph traversal results with vector similarity search scores using Reciprocal Rank Fusion (RRF) to produce a final ranked retrieval set for RAG context assembly

System Components

Dependency Parsing KG Constructor

Uses classical NLP dependency parsing to extract entities and relations from text, building a structured knowledge graph without LLM API calls, reducing cost and latency

Multi-Granular Embedding Store

Maintains separate vector indices for entities, document chunks, and relations, enabling retrieval at different semantic granularities depending on query type

Reciprocal Rank Fusion (RRF) Hybrid Retriever

Combines ranked results from graph traversal and vector similarity search using RRF to produce a unified, reranked context list for the RAG generator

LLM-as-Judge Evaluator

Uses an LLM to assess the quality of generated answers against ground truth on enterprise datasets, providing domain-relevant evaluation of retrieval quality

Results

Metric/Benchmark	Baseline	This Paper	Delta
KG Extraction Quality (F1/similarity)	65.83% (LLM-based)	61.87% (dep. parsing)	-3.96% (94% relative)
RAG Quality - Dataset 1 (LLM-as-Judge)	Vanilla vector retrieval	+15% improvement	+15%
RAG Quality - Dataset 2 (LLM-as-Judge)	Vanilla vector retrieval	+4.35% improvement	+4.35%

Key Takeaways

Classical NLP dependency parsing can replace LLM-based KG construction at ~94% quality, offering a compelling cost-performance tradeoff for production GraphRAG systems
Hybrid retrieval with RRF over multi-granular embeddings (entities + chunks + relations) consistently outperforms pure vector search and is straightforward to implement on top of existing RAG infrastructure
Maintaining separate embeddings at different semantic granularities (entity-level, chunk-level, relation-level) is a practical design pattern that enables more flexible and accurate retrieval for complex queries

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 due to reliance on expensive large language model (LLM)-based extraction and complex traversal strategies. To address these challenges, we introduce two core innovations: (1) an efficient knowledge graph construction pipeline that leverages dependency parsing to achieve 94% of LLM-based performance (61.87% vs. 65.83%) while significantly reducing costs and improving scalability; and (2) a hybrid retrieval strategy that fuses vector similarity with graph traversal using Reciprocal Rank Fusion (RRF), maintaining separate embeddings for entities, chunks, and relations to enable multi-granular matching. We evaluate our framework on two enterprise datasets focused on legacy code migration and demonstrate improvements of up to 15% and 4.35% over vanilla vector retrieval baselines using LLM-as-Judge evaluation metrics. These results validate the feasibility of deploying GraphRAG in production enterprise environments, demonstrating that careful engineering of classical NLP techniques can match modern LLM-based approaches while enabling practical, cost-effective, and domain-adaptable retrieval-augmented reasoning at scale.