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A Systematic Exploration of Knowledge Graph Alignment with Large Language Models in Retrieval Augmented Generation

This paper explores the alignment of knowledge graphs with large language models in retrieval augmented generation.

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A Systematic Exploration of Knowledge Graph Alignment with Large Language Models in Retrieval Augmented Generation

By Shiyu Tian, Shuyue Xing, Xingrui Li, Yangyang Luo, Caixia Yuan, Wei Chen, Huixing Jiang, Xiaojie WangProceedings of the AAAI Conference on Artificial Intelligence
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The authors investigate the factors affecting knowledge graph alignment with large language models, including graph transformation and linearization phases. They conduct experiments on 15 typical LLMs and three common datasets to identify optimal factors for improvement.

The study finds that centrality of the KG, formats, orders, and templates significantly impact KGA.

Abstract

The authors investigate the factors affecting knowledge graph alignment with large language models, including graph transformation and linearization phases. They conduct experiments on 15 typical LLMs and three common datasets to identify optimal factors for improvement. The study finds that centrality of the KG, formats, orders, and templates significantly impact KGA.

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knowledge graph alignmentlarge language modelsretrieval augmented generationgraph transformationlinearization phaseoptimizing KGAKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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