Graph Neural Networks - Graph Attention Networks on Knowledge Graphs with GraphRAG for Large Language Model Reasoning

Learn about KERAG_R, an innovative framework that combines Graph Neural Networks with Large Language Models for enhanced recommendation systems in this 27-minute tutorial. Explore how Graph Attention Networks (GAT) analyze knowledge graphs to retrieve the most relevant factual triples from user interactions, then discover how this structured knowledge integrates into prompts for fine-tuning Large Language Models using LoRA Adapters. Understand the methodology behind generating factually grounded and contextually aware recommendations by reasoning over both user behavior and explicit domain facts. Examine applications across various domains from finance to healthcare, and gain insights into the research conducted by Zeyuan Meng, Zixuan Yi, and Iadh Ounis from the University of Glasgow on knowledge-enhanced retrieval-augmented generation for recommendation systems.