## 3 Discussion and Conclusions  
There is a growing demand for healthcare services to help people make informed choices about their wellbeing, including the foods and drinks they consume. Improper eating can contribute to or precipitate diseases, such as diabetes, hypertension, some types of cancer, and some types of allergies, among others [18]. In order to enable lay users and healthcare professionals to gain access to relevant medical knowledge about food products in e-health systems that integrate different technologies and data sources, semantic frameworks containing machine-readable annotations (ontology) about food and other relevant and related domains (e.g., clinical medicine, dietetics, individual user profiles, _etc._) are critical for the successful delivery of such smart (context-aware) e-health systems. The ultimate goal is to progress beyond the mere identification of the full details of what is on the user's plate (what food scanning methods connected to cloud-based lookup databases can do) to answering the key question about \"how good or healthy is the food on the user's plate (or in his/her supermarket shopping basket), given the user's individual health condition, personal needs and preferences\" and making recommendations for specific, personalized dietary improvements as necessary. In this paper, we reviewed some \"food ontologies\" and the associated methods and tools they employ, as well as some of their applications. In the same vein as the afore described \"Web-Based Food Menu Recommender System for Patients with Diabetes in Thailand\", [PERSON] _et al._[39] proposed an ontology-based personalized dietary recommendation system for weightlifting athletes, while [PERSON] _et al._[40] presented a chronic disease dietary consultation system using ontologies and semantic rules. These ontologies and their applications hint at the potential of the \"powerful and smarter semantic reasoning with food data\" that can be achieved if such ontologies are combined with other relevant non-food ontologies (e.g., ontologies about diet-sensitive disease conditions--_cf._[14, 15, 16]). Although none of the reviewed ontologies is fully comprehensive in terms of scope and coverage (some are meant for very specific uses, e.g., in diabetes, and locales, e.g., Thailand), they remain good examples to learn from and might also form a basis, to be added to and further expanded, for future developments towards a universal comprehensive \"smart diet assessment and recommendation\" engine/application for use by consumers and patients with various diet-sensitive conditions worldwide (_i.e.,_ international coverage). Indeed, one of our main reasons for embarking on a review of existing food ontologies was to document their coverage gaps (the incompleteness or limited scope of existing food ontologies could negatively affect the performance of any wider-scope reasoning engine that relies on them). These coverage gaps justify the need, as a future research direction, for developing a universal, comprehensive food ontology (and the appropriate mechanisms for maintaining and updating it), while learning from previous attempts and approaches in this domain. Future developments should also cover other relevant and equally comprehensive non-food ontologies (e.g., user model ontology, clinical dietetics and disease ontology, _etc._) to properly reason with food details in the context of individual users and disease conditions. Although differing but overlapping in coverage (each being more fit for its specific purposes), the five main ontologies presented in this article can also be seen as complementing each other in various ways. For example, Open Food Facts ontology [20] covers 50,448 packaged food products from 123 countries, while FoodWiki [18] focuses on packaged products in the Turkish market only. Both ontologies use OWL language, which means they can be integrated to extend the coverage of food products.