Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain DOI Open Access
Simon Knollmeyer, Oğuz Caymazer, Daniel Großmann

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(11), P. 2102 - 2102

Published: May 22, 2025

Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters robustness enhances answer generation by incorporating Knowledge Graphs (KGs) built upon document’s intrinsic structure into the pipeline. Through application of Design Science Research methodology, we systematically design, implement, evaluate GraphRAG, leveraging graph-based document structuring keyword-based semantic linking mechanism improve quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, newly developed manufacturing dataset, demonstrates consistent performance gains over naive baseline across both metrics. results indicate GraphRAG improves Context Relevance metrics, with task-dependent optimizations chunk size, keyword density, top-k further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured strategy, highlighting its advantages complex reasoning tasks.

Language: Английский

Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain DOI Open Access
Simon Knollmeyer, Oğuz Caymazer, Daniel Großmann

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(11), P. 2102 - 2102

Published: May 22, 2025

Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters robustness enhances answer generation by incorporating Knowledge Graphs (KGs) built upon document’s intrinsic structure into the pipeline. Through application of Design Science Research methodology, we systematically design, implement, evaluate GraphRAG, leveraging graph-based document structuring keyword-based semantic linking mechanism improve quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, newly developed manufacturing dataset, demonstrates consistent performance gains over naive baseline across both metrics. results indicate GraphRAG improves Context Relevance metrics, with task-dependent optimizations chunk size, keyword density, top-k further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured strategy, highlighting its advantages complex reasoning tasks.

Language: Английский

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