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A Retrieval-Augmented Generation Framework Based on a Knowledge Graph of Cybersecurity Vulnerabilities in Power Networks

Proposes a retrieval-augmented generation framework for power grid cybersecurity.

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A Retrieval-Augmented Generation Framework Based on a Knowledge Graph of Cybersecurity Vulnerabilities in Power Networks

By Xingzheng Gao, Xing ChangIEEE Access
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This paper presents a framework that integrates knowledge graphs with large language models to enhance their accuracy and practicality in decision support. The framework consists of seven key steps, including knowledge modeling, extraction, storage, and problem-solving.

Experimental evaluation demonstrates the effectiveness of the proposed framework, achieving high scores across six metrics.

Abstract

This paper presents a framework that integrates knowledge graphs with large language models to enhance their accuracy and practicality in decision support. The framework consists of seven key steps, including knowledge modeling, extraction, storage, and problem-solving. Experimental evaluation demonstrates the effectiveness of the proposed framework, achieving high scores across six metrics.

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cybersecuritypower-gridknowledge-graphlarge-language-modelretrieval-augmented-generationKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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