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.

翻译：由于更多地使用AI方法在健康方面提供建议,特别是在食物饮食建议空间,因此也越来越需要解释这些建议。这种解释将有利于建议系统的用户,赋予他们遵循系统建议的理由; 我们提出食品解释本体学(FEO),为与食品有关的建议的用户提供示范性解释提供形式; FEO 粮食建议模型,利用解释领域的概念,就从AI系统(如个性化知识基础问题回答系统)收到的关于食品建议的用户问题提出答复; FEO使用模块化的、可扩展的结构,便于提供各种解释,同时保留重要的语义细节以准确代表食品建议的解释; 为了评估这一系统,我们使用了一套从与食品建议有关的文献中的解释类型中得出的能力问题; 我们利用FEO的动机是使用户能够就自己的健康作出决定,充分理解AI建议系统与用户问题有关,以解释的形式提供理由。