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Document Knowledge Graph to Enhance Question Answering with Retrieval Augmented Generation

A paper proposing a concept to enhance Retrieval Augmented Generation systems by integrating a Knowledge Graph constructed from document structures.

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Document Knowledge Graph to Enhance Question Answering with Retrieval Augmented Generation

By Simon Knollmeyer, Muhammad Uzair Akmal, Leonid Koval, Saara Asif, Selvine G. Mathias, Daniel Großmann
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The authors propose an approach to improve question answering in the factory planning domain using a knowledge graph and retrieval augmented generation. They aim to address limitations of existing RAG implementations that rely on vector databases.

The proposed concept integrates a knowledge graph constructed from document structures to provide more accurate answers.

Abstract

The authors propose an approach to improve question answering in the factory planning domain using a knowledge graph and retrieval augmented generation. They aim to address limitations of existing RAG implementations that rely on vector databases. The proposed concept integrates a knowledge graph constructed from document structures to provide more accurate answers.

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question-answeringdocument-knowledge-graphretrieval-augmented-generationknowledge-graph-constructionKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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