Using LLMs to Enrich and Validate Nutrition Knowledge Graphs
May 21, 2025 2025-06-13 10:38
Using LLMs to Enrich and Validate Nutrition Knowledge Graphs
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Abstract: The integration of Large Language Models (LLMs) with nutrition knowledge graphs (KGs) is revolutionizing the way we structure and interpret food-related data. By automating the extraction and linking of nutritional information, LLMs enhance the scalability and accuracy of KGs, facilitating more personalized and informed dietary recommendations.
This presentation explores recent advancements in applying LLMs to enrich and validate nutrition KGs, highlighting methodologies such as entity recognition, ontology alignment, and constraint relaxation. We will examine case studies demonstrating the practical applications of these techniques in improving dietary guidance and public health outcomes. The discussion will also address current challenges and future directions in this rapidly evolving field.
Lecturer: Riste Stojanov
Link for remote participants: www.teams.microsoft.com
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