FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation

Haussmann, Steven
Seneviratne, Oshani
Chen, Yu
Ne'eman, Yarden
Codella, James
Chen, Ching Hua
McGuinness, Deborah L.
Zaki, Mohammed
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Health Empowerment by Analytics, Learning, and Semantics (HEALS)
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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. We describe the construction process of our knowledge graph, the plan for its maintenance, and how this knowledge graph has been utilized in several applications. These applications include a SPARQL-based service that lets a user determine what recipe to make based on ingredients at hand while taking constraints such as allergies into account, as well as a cognitive agent that can perform natural language question answering on the knowledge graph.