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TKG-RAG: A Retrieval-Augmented Generation Framework with Text-chunk Knowledge Graph

A retrieval-augmented generation framework that utilizes a text-chunk knowledge graph to improve performance.

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TKG-RAG: A Retrieval-Augmented Generation Framework with Text-chunk Knowledge Graph

By Xiao Wei, Yu Liu, Xianglong Li, Feng Gao, Jinguang Gu
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The authors propose TKG-RAG, a framework that constructs a text-chunk knowledge graph automatically from domain text. This framework improves Retrieval-Augmented Generation (RAG) performance by addressing limitations such as noise and redundant information in retrieved text chunks.

Comparative experiments show that TKG-RAG achieves better accuracy and F1 scores while reducing token consumption.

Abstract

The authors propose TKG-RAG, a framework that constructs a text-chunk knowledge graph automatically from domain text. This framework improves Retrieval-Augmented Generation (RAG) performance by addressing limitations such as noise and redundant information in retrieved text chunks. Comparative experiments show that TKG-RAG achieves better accuracy and F1 scores while reducing token consumption.

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tkgragtext-chunk knowledge graphretrieval-augmented generationframeworkperformance improvementKnowledge GraphsLarge Language ModelsRetrieval & RAGContent Engineering
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