Semantic Modeling for Food Recommendation Explanations
  With the increased use of AI methods to provide recommendations in the
health, specifically in the food dietary recommendation space, there is also an
increased need for explainability of those recommendations. Such explanations
would benefit users of recommendation systems by empowering them with
justifications for following the system's suggestions. We present the Food
Explanation Ontology (FEO) that provides a formalism for modeling explanations
to users for food-related recommendations. FEO models food recommendations,
using concepts from the explanation domain to create responses to user
questions about food recommendations they receive from AI systems such as
personalized knowledge base question answering systems. FEO uses a modular,
extensible structure that lends itself to a variety of explanations while still
preserving important semantic details to accurately represent explanations of
food recommendations. In order to evaluate this system, we used a set of
competency questions derived from explanation types present in literature that
are relevant to food recommendations. Our motivation with the use of FEO is to
empower users to make decisions about their health, fully equipped with an
understanding of the AI recommender systems as they relate to user questions,
by providing reasoning behind their recommendations in the form of
explanations.
