Technical Background
FoodAtlas behind the Scenes
FoodAtlas is an AI-powered tool that maps the complex relationships between food, chemicals, and diseases. It not only identifies the types and quantities of chemicals in the foods we consume but also explores their potential health impacts.
Our system continuously monitors new research, extracting data on chemical concentrations and disease correlations, which are then integrated into our knowledge base. This data is cross-referenced with established databases, such as PubChem, and incorporated into our knowledge graph.
The following provides a brief overview of some of the methods and technologies used–for a detailed look behind the scenes, refer to our first publication.
Knowledge Graph
FoodAtlas uses a knowledge graph to store and organize the vast number of interconnected entities including foods, chemicals, and their relations. Each connection constitutes a triplet–a row in our knowledge base–containing a head, a tail, and their relationship.
In the closeup, Garlic connects to three entities and Garlic root to one entity, forming 4 triplets.
Graph Semantics
A node is either a Food , a Chemical , or a Disease .
An edge informs on the relationship between two nodes. FoodAtlas captures contains relations, i.e. what chemicals are found in certain foods. Those chemicals may then either positively/negatively correlate with a disease. Our next iteration adds taxonomical information with is a , and extends positively/negatively correlates to foods containing chemicals associated with diseases.
Pipeline
Our pipeline uses state-of-the-art AI models to extract and quantify food connections. The two major steps are (a) knowledge extraction, i.e., converting literature into food-chemical relations and (b) knowledge graph construction , which adds metainformation and new information to our knowledge base.
Knowledge Extraction
Knowledge Graph Construction
Output is converted into triplets, the building block of the knowledge graph data structure
Triplets are linked to existing corresponding entities, or new ones are created
Metadata such as concentration values, food parts, external references, and quality scores is compiled
About AIFS
The AI Institute for Next Generation Food Systems, or AIFS aims to meet growing demands in our food supply by increasing efficiencies using Al and bioinformatics spanning the entire system–from growing crops through consumption. We are dedicated to creating AI applications for a healthier, more sustainable planet from farm to fork.
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