LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
LeanRAG is a novel Retrieval-Augmented Generation (RAG) framework designed to overcome limitations in existing knowledge graph-based RAG methods, specifically "semantic islands" and structurally unaware retrieval. It integrates a semantic aggregation algorithm with a bottom-up, structure-guided hierarchical retrieval strategy. This approach aims to provide concise yet contextually comprehensive evidence sets for Large Language Models (LLMs), significantly improving response quality and reducing retrieval redundancy. ✨
Article Points:
1
Novel semantic aggregation algorithm for superior knowledge condensation.
2
Constructs multi-resolution KG with explicit inter-cluster relations.
3
Bottom-up, structure-aware retrieval minimizes information redundancy.
4
Anchors queries to fine-grained entities, traverses semantic pathways.
5
Achieves state-of-the-art performance on diverse QA benchmarks.
6
Reduces retrieval redundancy by 46% while improving response quality.
LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Challenges Addressed
Semantic Islands
Structurally Unaware Retrieval
Information Redundancy
Core Innovations
Semantic Aggregation
Structured Retrieval
Methodology
Hierarchical KG Aggregation
LCA Path Traversal
Experimental Results
State-of-the-Art Performance
Reduced Redundancy (46%)
Inter-cluster Relations Impact
Textual Context Necessity
Key Contributions
Novel Aggregation Algorithm
Bottom-up Retrieval Strategy
SOTA QA Performance
Implementation