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Enhancing Retrieval Augmented Generation Systems with Knowledge Graphs

A paper proposing a comprehensive approach to enriching knowledge graphs.

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Enhancing Retrieval Augmented Generation Systems with Knowledge Graphs

By Tavva Prudhvith, Chakrabarty Swattik, Selvakumar Prakash
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The authors introduce a methodology that integrates key phrase extraction, node embedding generation, and an autonomous updating agent to create a connected knowledge graph. They also explore the incorporation of traditional vector search to enhance contextual understanding.

The results show a substantial improvement in accuracy compared to traditional KG approaches.

Abstract

The authors introduce a methodology that integrates key phrase extraction, node embedding generation, and an autonomous updating agent to create a connected knowledge graph. They also explore the incorporation of traditional vector search to enhance contextual understanding. The results show a substantial improvement in accuracy compared to traditional KG approaches.

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knowledge graphsinformation retrievalgraph databasesnatural language processingKnowledge GraphsContent EngineeringAI AgentsLarge Language Models
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Enhancing Retrieval Augmented Generation Systems with Knowledge Graphs | Aramai