FoodWiki: Ontology‐Driven Mobile Safe Food Consumption System

Çelik, Duygu

doi:10.1155/2015/475410

Cited by 25 publications

(8 citation statements)

References 11 publications

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“…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.)…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we review the following food ontologies: FoodWiki [18], AGROVOC [19], Open Food Facts [20], Food Product Ontology [21], and FOODS (Diabetes Edition) [22]. Table 1 below presents a summary of these methods in terms of their various properties and characteristics.…”

Section: Food Ontologiesmentioning

confidence: 99%

“…FoodWiki [18] is an ontology-driven, safe food consumption mobile e-health system. It allows users/patients in risk groups to monitor and control their food intake.…”

Section: Foodwikimentioning

confidence: 99%

“…In order to perform the semantic enhancement task, the system parses appropriate concepts and relationships defined in the food ingredients section of the ontology. Specifically, concepts related to food ingredients and their properties are processed according to the terms in the Consumer Intolerance List [18].…”

Section: Foodwikimentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Food Ontologiesmentioning

confidence: 99%

“…FoodWiki [18] is an ontology-driven, safe food consumption mobile e-health system. It allows users/patients in risk groups to monitor and control their food intake.…”

Section: Foodwikimentioning

confidence: 99%

Section: Foodwikimentioning

confidence: 99%

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

et al. 2015

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Knowledge representation and data sharing to unlock crop variation for nutritional food security

et al. 2020

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Meeting the challenge of food and nutritional security requires ongoing innovation, particularly in managing dietary nutritional information for pre-breeding analysis, selection, and cultivation of specific food crops and cultivars. At present, the ability to compare the relative nutritional value of crops is limited, with data management systems for most crops often inconsistent and poorly integrated. Here, we review generic efforts to standardize the description and management of crop trait data and discuss several issues currently constraining their exchange and comparison, with a focus on knowledge representation related to dietary nutrition. These issues include lack of consistency within or between crop specific databases, as well as limited data standardization and interoperability. At present, the use of common descriptors or controlled vocabularies between crops is fragmentary, with only partial implementation or uptake of formal ontologies, particularly for dietary nutritional composition. Although development of the existing Crop Ontology (CO) system has improved data sharing and reuse, it represents only a limited set of trait classes and crops. We identify the need for more robust and generic ontologies, particularly those that may address crop contributions to human dietary nutrition. We propose development of a Crop Dietary Nutrition Ontology (CDNO) as a robust structured controlled vocabulary for dietary nutritional composition and function, and provide examples of specific use cases and different end users who would benefit from using CDNO terms in their database searches. This development is likely to transform the way in which crops may be compared in terms of optimal dietary nutritional values.

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