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FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation

The proliferation of recipes and other food information on the Web presents an opportunity for discovering and organizing diet-related knowledge into a knowledge graph. Currently, there are several ontologies related to food, but they are specialized in specific domains, e.g., from an agricultural, production, or specific health condition point-of-view. There is a lack of a unified knowledge graph that is oriented towards consumers who want to eat healthily, and who need an integrated food suggestion service that encompasses food and recipes that they encounter on a day-to-day basis, along with the provenance of the information they receive. Our resource contribution is a software toolkit that can be used to create a unified food knowledge graph that links the various silos related to food while preserving the provenance information.

Resources

Demo Videos: Demonstration videos shared here.
FoodKG construction: we describe the construction process of our knowledge graph and make available all the scripts that were used.
What To Make Ontology and Application: An Ontology modeled to to let a user determine what recipe to make based on ingredients at hand while taking constraints such as allergies into account.
Answering Natural Language Questions over FoodKG: A cognitive agent that can perform natural language question answering on the knowledge graph.

Knowledge Graph Store

To load the integrated knowledge graph that will be generated using the software we have provided, we recommend using Blazegraph. Please follow the instructions in the User Guide to download, install, and load the RDF data in to the Blazegraph endpoint on your system.

Optionally, you may use whyis as your knowledge graph store.

Team

FoodKG is a research and development effort of the Health Empowerment by Analytics Learning and Semantics (HEALS) project.

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