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Contextual and Semantic Information Retrieval using LLMs and Knowledge Graphs

Phani Dathar, Director Graph Data Science | Neo4j It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. Enterprises see them as an opportunity to automate many tasks, improve customer experience, and expedite content retrieval, generation, and summarization. But, did you know that combining Knowledge Graphs and Generative AI unlocks even more value? Together, they enable contextual and semantic information retrieval from both structured and unstructured data sources. Join our session to understand how the deep dynamic context that Knowledge Graphs provide helps ensure answers from an LLM are accurate, explainable, and contextual. You’ll learn how LLMs and Neo4j labeled property graphs synergize seamlessly, whether for querying your enterprise graph with natural language or converting unstructured data into a knowledge graph.