Large Language Models (LLMs) are powerful but notoriously ungrounded as they generate fluent, plausible text that isn’t always factual, consistent, or explainable. One promising way to address these issues is by connecting LLMs with Knowledge Graphs, namely structured, explicit representations of knowledge that can provide context, constraints, and verifiable facts. The main paradigm for doing this is Graph-based Retrieval-Augmented Generation (GraphRAG), which integrates graph-based reasoning and retrieval into the generation process. In this talk, I’ll introduce the core ideas behind GraphRAG, describe common design patterns, and outline the steps and tools needed to implement such systems in practice.
talk-data.com
Speaker
Panos Alexopoulos
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Data Semantics & AI Specialist | Author | Educator
Triply BV
Panos Alexopoulos is a Data and AI practitioner, author, and educator, with 20 years of industry experience across diverse domains. His expertise lies at the intersection of semantic data modeling, data quality, and development and evaluation of AI systems. Currently he works as Lead Semantic Data and AI solutions at Triply BV, in Amsterdam, Netherlands, where he helps large organizations design, develop and deploy data management and AI solutions. He is also the author of Semantic Modeling for Data (O’Reilly, 2020), a well received and highly rated book in the semantic technology and knowledge graph communities. In the last few years he has designed and delivered over 20 masterclasses, tutorials, and courses on data and AI, including a highly popular course on Knowledge Graphs and Large Language Models.
Bio from: 10 PyData Piraeus Meetup: LLMs with Knowledge Graphs and Information Retrieval
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