Other resources that we have found useful.
Hsiao Lab is developing LexMapr, a program for converting shorter biosample text descriptions to an ontology representation. It uses FoodOn, Envo, and other ontologies, and is initially focused on food sample descriptions.
The Global Names Architecture (GNA) is a system of web-services which helps people to register, find, index, check and organize biological scientific names and interconnect on-line information about species. https://index.globalnames.org/
FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration
Our inaugural FoodOn paper, available as a pdf at https://rdcu.be/bdLun or on the web at https://www.nature.com/articles/s41538-018-0032-6 .
FOOD: FOod in Open Data
A recent linked data effort to represent Italian products that have protected names (wine, pasta, fish, oil, etc.).
Fungi as a source of Food
A thorough introduction to a wide range of human food Fungi applications.
An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content (2019)
A recent ontology development effort focused on the adequate description of nutritional studies with an epidemiology aspect.
ONS: an ontology for a standardized description of interventions and observational studies in nutrition. (2019)
This paper introduces the Ontology for Nutritional Studies (ONS), a Basic Formal Ontology compatible ontology that harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications in order to describe nutritional studies.
Mapping Food Composition Data from Various Data Sources to a Domain Specific Ontology (2017)
Explores details of how nutrients are referenced in Food composition data (FCD) , specifically EuroFIR and USDA National Nutrient databases, and provides a new Quisper ontology to capture this information.
A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations (2017)
Introduces drNER, a new named-entity recognition (NER) method that expresses food quantity and units found in text, based on NLP part of speech matching.
NOVA. The star shines bright (2016)
NOVA is the food classification that categorizes foods according to the extent and
purpose of food processing, rather than in terms of nutrients. It classifies all foods and food products into four clearly distinct and in the authors’ view meaningful groups.
ISO-FOOD ontology: A formal representation of the knowledge within the domain of isotopes for food science (2019)
Focuses on the enumeration of certain stable isotopes and chemical elements within food (organic) compounds, in a new ISO-FOOD ontology.
The unmapped chemical complexity of our diet (2019)
A position paper exploring the impact and quantity of other chemical compounds besides the more well documented 150+ nutritional components tracked by common food composition databases.
FoodBase corpus: a new resource of annotated food entities (2019)
“A new annotated corpus of food entities, named FoodBase. It was constructed using recipes extracted from Allrecipes, which is currently the largest food-focused social network. The recipes were selected from five categories: ‘Appetizers and Snacks’, ‘Breakfast and Lunch’, ‘Dessert’, ‘Dinner’ and ‘Drinks’. Semantic tags used for annotating food entities were selected from the Hansard corpus.”