CC BY 4.0Rincon-Yanez, DiegoDiegoRincon-YanezSchmidt, WilmaWilmaSchmidtKharlamov, EvgenyEvgenyKharlamovCochez, MichaelMichaelCochezPaschke, AdrianAdrianPaschkeO'Sullivan, DeclanDeclanO'Sullivan2025-11-112025-11-112025-091613-0073https://publica.fraunhofer.de/handle/publica/499045https://doi.org/10.24406/publica-621010.24406/publica-6210This version explores the intersection of Knowledge Graphs (KGs) and Large Language Models (LLMs) with a focus on enabling scalable, efficient, and trustworthy AI applications in industrial contexts. As generative AI rapidly evolves, integrating symbolic and neural methods becomes essential to address challenges such as explainability, data alignment, and system robustness by gathering academic researchers and industry practitioners to discuss practical solutions and future of Semantic Web technologies in the era of foundation models.enKnowledge GraphsLarge Language ModelsGraph Retrieval Augmented GenerationScalable AIAI for Industryconference paper