NGM-RAG: Neural Graph Matching for Retrieval-Augmented Generation
_reachsumit · x · 2026-07-14
NGM-RAG: Retrieval-Augmented Generation Based on Neural Graph Matching
This work proposes a graph matching retrieval framework for RAG that combines two types of signals:
- Text similarity: Filtering candidate contexts based on semantic relevance first
- GNN structural matching: Performing structural-level matching using Graph Neural Networks
The author aims to retrieve more relevant contexts through this approach, with application scenarios including:
- Multi-hop QA
- Long-context summarization
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