Towards an “Internet of Food”: Food Ontologies for the Internet of Things

Abstract

Automated food and drink recognition methods connect to cloud-based lookup databases (e.g., food item barcodes, previously identified food images, or previously classified NIR (Near Infrared) spectra of food and drink items databases) to match and identify a scanned food or drink item, and report the results back to the user. However, these methods remain of limited value if we cannot further reason with the identified food and drink items, ingredients and quantities/portion sizes in a proposed meal in various contexts; i.e., understand from a semantic perspective their types, properties, and interrelationships in the context of a given user's health condition and preferences. In this paper, we review a number of "food ontologies", such as the Food Products Ontology/FOODpedia (by Kolchin and Zamula), Open Food Facts (by Gigandet et al.), FoodWiki (Ontology-driven Mobile Safe Food Consumption System by Celik), FOODS-Diabetes Edition (A Food-Oriented Ontology-Driven System by Snae Namahoot and Bruckner), and AGROVOC multilingual agricultural thesaurus (by the UN Food and Agriculture Organization-FAO). These food ontologies, with appropriate modifications (or as a basis, to be added to and further OPEN ACCESSFuture Internet 2015, 7 373 expanded) and together with other relevant non-food ontologies (e.g., about diet-sensitive disease conditions), can supplement the aforementioned lookup databases to enable progression from the mere automated identification of food and drinks in our meals to a more useful application whereby we can automatically reason with the identified 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 (Body Mass Index), lifestyle and preferences, etc.

Disruption of Things: A Model to Facilitate Adoption of IoT-based Innovations by the Urban Poor

“…Boulos et. al [44] showed that automated food and drink recognition techniques that connected to cloud-based databases could match the near-infrared spectra of food and drink can be used to recognize food items. These recognition techniques combined with automated measurement of portion sizes and ingredient nutrition information would enable devices to associate nutrition information with specific foods and guide users to appropriate diet items.…”

Utilization of text mining as a big data analysis tool for food science and nutrition

“…Ontology, defined as formal naming of a set of concepts within a domain, has been widely used for knowledge discovery in the age of Semantic Web (Eftimov, Ispirova, Potočnik, Ogrinc, & Seljak, 2019). As we move quickly toward the Internet of Things (IoT) paradigm, advancing food ontology would provide effective communications of food, ingredients, and health outcomes from a semantic view (Boulos, Yassine, Shirmohammadi, Namahoot, & Brückner, 2015). A few examples of food ontologies designed for various purposes are Food-Wiki, AGROVOC, Open Food Facts, Food Product Ontology, and Foodon (Boulos et al, 2015;Dooley et al, 2018).…”

“…As we move quickly toward the Internet of Things (IoT) paradigm, advancing food ontology would provide effective communications of food, ingredients, and health outcomes from a semantic view (Boulos, Yassine, Shirmohammadi, Namahoot, & Brückner, 2015). A few examples of food ontologies designed for various purposes are Food-Wiki, AGROVOC, Open Food Facts, Food Product Ontology, and Foodon (Boulos et al, 2015;Dooley et al, 2018). For instance, FoodWiki is a system designed for customers to quickly examine the free text written on packaged food products for inferring their side effects (Çelik, 2015;Ertuğrul, 2016).…”

Ethics of Food Recommender Applications

“…Generally, IoF as a concept was envisioned as sophisticated communications and digital services between machines, consumers and companies through sensorisation to provide information about nearly all food ingredients and products [9]. IoF ranges from monitoring and executing steps in the food production via the combination of advanced sensory applications, advanced methods for collecting and interpreting data, and automation to the creation of digital information platforms based on which AI systems can give personalised recommendations to the consumer [30], [8]. For this reason, IoF is not limited to food recommender systems and therefore the proposed ethical architecture hardly brings us any closer to our goal than the general ethical guidelines: with its design suggesting a continuous reinforcement of these criteria, the IoF architecture is visualised as a circle from Privacy that leads to Transparency making Education possible, which enables Negotiability that brings Agency which resolves Responsibility that leads back to Privacy [30].…”