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Advancing engineering research through context-aware and knowledge graph–based retrieval-augmented generation

A study on improving the accuracy of large language models in generating technical content.

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Advancing engineering research through context-aware and knowledge graph–based retrieval-augmented generation

By Soham Ghosh, Gaurav MittalFrontiers in Artificial Intelligence
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The authors propose a new Retrieval-Augmented Generation (RAG) model for engineering domains, which uses contextual information to improve relevance. The model is built on the n8n automation system and can retrieve densely linked concepts from multiple knowledge graphs.

This approach aims to mitigate the shortcomings of traditional RAG techniques in treating isolated information.

Abstract

The authors propose a new Retrieval-Augmented Generation (RAG) model for engineering domains, which uses contextual information to improve relevance. The model is built on the n8n automation system and can retrieve densely linked concepts from multiple knowledge graphs. This approach aims to mitigate the shortcomings of traditional RAG techniques in treating isolated information.

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engineering researchcontext-aware retrievalknowledge graph-based generationretrieval-augmented generationtechnical accuracyKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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Advancing engineering research through context-aware and knowledge graph–based retrieval-augmented generation | Aramai