TL;DR
EHRAG introduces a hypergraph-based RAG framework that combines structural and semantic information for improved retrieval in lightweight graph-based LLM augmentation.
Contribution
It constructs a hybrid hypergraph capturing both structural and semantic relationships, enhancing retrieval effectiveness while maintaining low complexity.
Findings
EHRAG outperforms state-of-the-art baselines on four datasets.
It maintains linear indexing complexity and zero token cost for construction.
The framework effectively captures latent semantic connections between entities.
Abstract
Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recent lightweight approaches reduce indexing costs by leveraging Named Entity Recognition (NER), they rely strictly on structural co-occurrence, failing to capture latent semantic connections between disjoint entities. To address this, we propose EHRAG, a lightweight RAG framework that constructs a hypergraph capturing both structure and semantic level relationships, employing a hybrid structural-semantic retrieval mechanism. Specifically, EHRAG constructs structural hyperedges based on sentence-level co-occurrence with lightweight entity extraction and semantic hyperedges by clustering entity text embeddings, ensuring the hypergraph encompasses both structural and semantic information. For retrieval, EHRAG performs a structure-semantic hybrid…
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