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With the increased use of AI methods to provide recommendations in thehealth, specifically in the food dietary recommendation space, there is also anincreased need for explainability of those recommendations. Such explanationswould benefit users of recommendation systems by empowering them withjustifications for following the system's suggestions. We present the FoodExplanation Ontology (FEO) that provides a formalism for modeling explanationsto users for food-related recommendations. FEO models food recommendations,using concepts from the explanation domain to create responses to userquestions about food recommendations they receive from AI systems such aspersonalized knowledge base question answering systems. FEO uses a modular,extensible structure that lends itself to a variety of explanations while stillpreserving important semantic details to accurately represent explanations offood recommendations. In order to evaluate this system, we used a set ofcompetency questions derived from explanation types present in literature thatare relevant to food recommendations. Our motivation with the use of FEO is toempower users to make decisions about their health, fully equipped with anunderstanding of the AI recommender systems as they relate to user questions,by providing reasoning behind their recommendations in the form ofexplanations.

Semantic Modeling for Food Recommendation Explanations