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GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs

A framework that uses graph neural networks to enhance retrieval in knowledge graph question answering.

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GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs

By Costas Mavromatis, George Karypis
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The GNN-RAG framework utilizes lightweight graph neural networks for efficient graph retrieval. It learns to assign importance weights to nodes and their neighboring nodes, enabling effective handling of context from distant nodes.

Experimental results show improved retrieval performance on two widely used KGQA benchmarks, outperforming or matching GPT-4 performance.

Abstract

The GNN-RAG framework utilizes lightweight graph neural networks for efficient graph retrieval. It learns to assign importance weights to nodes and their neighboring nodes, enabling effective handling of context from distant nodes. Experimental results show improved retrieval performance on two widely used KGQA benchmarks, outperforming or matching GPT-4 performance.

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gnn-raggraph-neural-networkskgqalarge-language-modelsKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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