NutriGNN: Food Nutrient Prediction with an LLM Enriched Knowledge Graph

Abstract

A graph neural network approach to predicting missing nutrient values in food composition databases. By building a knowledge graph enriched with LLM-derived semantic relations between foods, NutriGNN improves representation learning and prediction quality, especially for low-resource food items with sparse nutritional data.