A Knowledge Graph for Semantic-Driven Healthiness Evaluation of Online Recipes

Tracking #: 3260-4474

This paper is currently under review
Charalampos Chelmis
Bedirhan Gergin

Responsible editor: 
Mehwish Alam

Submission type: 
Dataset Description
The proliferation of recipes on the Web presents an opportunity for developing AI methods to promote healthy nutrition of people using the Internet as a source of food inspiration. Recent research endeavors have resulted in the development of ontologies related to food, and algorithmic solutions for ingredient substitution. However, there is a lack of a resource oriented towards promoting research in semantic-based algorithmic meal plan recommendation and/or individual ingredient substitution that explicitly incorporates healthiness into the recommendation process. To address this gap, we present a knowledge graph comprising a large collection of recipes sourced from Allrecipes.com, their ingredients and corresponding nutritional information, social interactions metadata, and healthiness information calculated based on two international nutritional standards. We describe the construction process of our knowledge graph, and show its utility in quantitatively evaluating the healthiness of online recipes.
Full PDF Version: 
Under Review