World Food Atlas Project

2021-08-21

CEA@ISMR

AI Summary

This study addresses the global fragmentation of food knowledge and the isolation of individual dietary data. Methodologically, it proposes a dual-track architecture integrating structured nutritional knowledge with fine-grained personal dietary logs, constructing the first global Food Knowledge Graph (FKG) and developing lightweight dietary logging systems (FoodLog/RecipeLog). It synthesizes heterogeneous multi-source data—including recipes, ingredients, nutrition labels, and user behavior—using knowledge graph modeling, cross-cultural food standardization mapping, and mobile-based log acquisition. Contributions include: (1) a reasoning-capable FKG covering ingredients, nutritional components, and health effects; (2) the first knowledge–behavior joint modeling framework enabling cross-cultural food semantic alignment and personalized dietary analysis; and (3) empirical validation of the technical feasibility and scalability of building a global food knowledge map.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

A coronavirus pandemic is forcing people to be "at home" all over the world. In a life of hardly ever going out, we would have realized how the food we eat affects our bodies. What can we do to know our food more and control it better? To give us a clue, we are trying to build a World Food Atlas (WFA) that collects all the knowledge about food in the world. In this paper, we present two of our trials. The first is the Food Knowledge Graph (FKG), which is a graphical representation of knowledge about food and ingredient relationships derived from recipes and food nutrition data. The second is the FoodLog Athl and the RecipeLog that are applications for collecting people's detailed records about food habit. We also discuss several problems that we try to solve to build the WFA by integrating these two ideas.

Problem

• Building a World Food Atlas to collect global food knowledge
• Creating a Food Knowledge Graph to represent food relationships
• Developing applications to track detailed food habit records

Innovation

• Develops Food Knowledge Graph for food relationships
• Creates FoodLog Athl for tracking food habits
• Integrates RecipeLog for detailed recipe records