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Nodes2024

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GNN-RAG: Bridging Graph Reasoning and Language Understanding for Enhanced Knowledge Retrieval

Session Track: AI

Session Time:

Session description

Explore the GNN-RAG framework, which combines Graph Neural Networks and Large Language Models for advanced knowledge retrieval. This talk highlights how GNN-RAG leverages graph reasoning and language understanding to improve question answering over knowledge graphs. Discover the unique synergy of structured graph analysis and natural language processing in delivering precise and contextually rich answers. Ideal for AI enthusiasts and practitioners, this session unveils the future of hybrid AI systems in enhancing knowledge discovery and retrieval. Join us to see how GNN-RAG is revolutionizing AI-driven solutions!

Speaker

photo of Karrtik Iyer

Karrtik Iyer

Data Science Principal, Thoughtworks; Head of Data & AI Community, /tw India

Karrtik holds deep expertise in developing and deploying large language models (LLMs) and generative AI technologies. His work in these cutting-edge fields has enabled the creation of sophisticated NLP applications that have transformed business operations across various industries, from healthcare to finance. A frequent speaker at industry conferences and a dedicated mentor, Karrtik is committed to sharing his knowledge and fostering the next generation of AI talent.