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Enhancing Knowledge Graph Completion with Retrieval-Augmented Generation Using Large Language Models

A study introducing a framework for Knowledge Graph Completion using Large Language Models and Retrieval-Augmented Generation.

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Enhancing Knowledge Graph Completion with Retrieval-Augmented Generation Using Large Language Models

By Zhengzheng Wei, James A. Esquivel
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The authors propose an innovative framework for Knowledge Graph Completion leveraging Large Language Models. The framework treats KG triples as textual prompts to retrieve relevant information from knowledge bases, generating contextually accurate responses.

A retrieval reranking strategy refines predictions by incorporating outputs from a pre-trained KGC model.

Abstract

The authors propose an innovative framework for Knowledge Graph Completion leveraging Large Language Models. The framework treats KG triples as textual prompts to retrieve relevant information from knowledge bases, generating contextually accurate responses. A retrieval reranking strategy refines predictions by incorporating outputs from a pre-trained KGC model.

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knowledge graph completionlarge language modelsretrieval-augmented generationknowledge graph triplesentity and relation representationsKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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