The Foodome: Building a Comprehensive Knowledge Graph of Food


The Foodome addresses how to create deeper understanding and predictive intelligence about the relationships between how we talk and learn about food, and what we actually eat. Our aim is to build a food learning machine that comprehensively maps, for any given food, its form, function, production, distribution, marketing, science, policy, history, and culture (as well as the connections among all of these aspects). We are gathering and organizing a wide variety of data, including news/social content, recipes and menus, and sourcing and purchase information. We then use human-machine learning to uncover patterns within and among the heterogeneous food-related data. Long term, the Foodome is meant to help improve our understanding of, access to, and trust in food that is good for us; find new connections between food and health; and even predict impacts of local and global events on food.