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

arXiv: https://arxiv.org/abs/2507.03226 · HTML: https://mlsanity.com/paper/2507.03226/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://mlsanity.com/paper/2405.14831/) (2024)
- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Seven Failure Points When Engineering a Retrieval Augmented Generation System](https://mlsanity.com/paper/2401.05856/) (2024)
- [RAG Does Not Work for Enterprises](https://mlsanity.com/paper/2406.04369/) (2024)
- [SubGCache: Accelerating Graph-based RAG with Subgraph-level KV Cache](https://mlsanity.com/paper/2505.10951/) (2025)

## Built on by (most cited first, 9 of 9)

- [Query-Centric Graph Retrieval Augmented Generation](https://mlsanity.com/paper/2509.21237/) (2025)
- [TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?](https://mlsanity.com/paper/2605.18025/) (2026)
- [CacheMind: From Miss Rates to Why -- Natural-Language, Trace-Grounded Reasoning for Cache Replacement](https://mlsanity.com/paper/2602.12422/) (2026)
- [FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs](https://mlsanity.com/paper/2606.19710/) (2026)
- [Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs](https://mlsanity.com/paper/2606.30093/) (2026)
- [Coarse Indexing, Fine Evidence: Decoupling Temporal Granularity in Long-Video RAG](https://mlsanity.com/paper/2608.23011/) (2026)
- [Beyond Vector Similarity: Hierarchical Context-Aware Graph RAG vs Standard RAG in Enterprise Code Migration](https://mlsanity.com/paper/2609.12464/) (2026)
- [The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora](https://mlsanity.com/paper/2609.24885/) (2026)
- [ConRAG: Lightweight inference of multi-hop relations](https://mlsanity.com/paper/2609.35193/) (2026)

## Abstract (arXiv)

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.