## 1 Introduction
Furthermore, \"measured doses of calorie burning via exercise\" can be prescribed in the form of tailored and personalized physical activities/exercise regimes, as necessary, based on not just users' BMI, current calorie intake and expenditure/activity patterns, but also their general health condition and other individual requirements such as hip/knee joint issues, old age and frailty, pollen allergy (workout routes prescribed based on latest pollen forecasts and where needed alternative routes are suggested), _etc._  
This kind of advanced application and reasoning requires a detailed knowledge of (or ontologies about): (i) individual user's characteristics and health status/medical history; and (ii) the best, current clinical evidence about nutrition/dietetics and the various disease conditions to be covered; coupled with (iii) knowledge about different foods and drinks, local cuisine characteristics/cooking habits, commercial food/drink product offerings and variants, _etc._, all of which can vary by locale/country and region of the world. This variation by locale should also be taken into consideration, given that people frequently travel these days for short or prolonged periods for work or leisure. These complementary types of knowledge and the corresponding ontologies are key to delivering a smart \"Internet of Food\" that can serve situation, context and user-specific diet insights and \"intelligent\" recommendations to users based on their actual individual and health needs, circumstances and profiles at any given time, similar to the approach described in [14, 15, 16] combining multiple ontologies and reasoning with them together. For example, if gluten is identified as part of the composition of a scanned food item (from a _food ontology_) _and_ we know that the user who is about to ingest that food suffers from coeliac disease (from user's health profile/medical history instance of a _user model ontology_) _and_ we know that eating foods containing gluten can trigger a range of mild to severe symptoms in people with celiac disease (from best current clinical evidence in a _disease ontology_), we can then warn the user that s/he should avoid eating that food item. An ontology is a formal naming of a set of concepts within a domain and it is used to infer about the properties of that domain [16, 17]. As we are rapidly moving towards the Internet of Things (IoT) paradigm, where the deployment of various devices in smart e-health systems faces interoperability and flexibility challenges, ontologies will play an important role in facilitating knowledge sharing and understanding among various interrelated subsystems and knowledge silos. In smart e-health systems, food ontologies offer efficient and flexible means to capture knowledge about dietary concepts, and would therefore provide us with a key required component for designing and implementing the said advanced application. In the rest of this paper, we start by reviewing a number of existing \"food ontologies\", followed by a discussion of their coverage gaps and their potential reuse or adaptation, (as a model to follow, add to,further expand, and combine with other relevant non-food ontologies), in a universal application to automatically reason with scanned food and drink items and their details (quantities and ingredients/bromatological composition) in order to better assist users in making the correct, healthy food and drink choices for their particular health condition, age, body weight/BMI, lifestyle and preferences.