FOBI (Food-Biomarker Ontology) is composed of two interconnected sub-ontologies. One is a ”Food Ontology” consisting of raw foods and multiple-ingredient foods while the second is a ”Biomarker Ontology” containing food intake biomarkers classified by their chemical classes. These two sub-ontologies are conceptually independent but interconnected by different properties. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows (e.g. metabolite identification, experimental design), or the application of different enrichment analysis approaches to analyze nutrimetabolomic data.

The SEPIO ontology is in its early stages of development, undergoing iterative refinement as new requirements emerge and alignment with existing standards is explored.

Chemical Entities of Biological Interest, also known as ChEBI,[1][2] is a chemical database and ontology of molecular entities focused on 'small' chemical compounds, that is part of the Open Biomedical Ontologies (OBO) effort at the European Bioinformatics Institute (EBI). The term "molecular entity" refers to any "constitutionally or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity".[3] The molecular entities in question are either products of nature or synthetic products which have potential bioactivity. Molecules directly encoded by the genome, such as nucleic acids, proteins and peptides derived from proteins by proteolytic cleavage, are not as a rule included in ChEBI.

ChEBI uses nomenclature, symbolism and terminology endorsed by the International Union of Pure and Applied Chemistry (IUPAC) and nomenclature committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB).

The fire science ontology was developed with input from domain experts in  fire science with a focus on concepts important for prescribed fires. Datasets in wifire commons are semantically tagged with terms from the ontology which aid in discoverability. As the BurnPro3d platform evolves and new datasets are added to wifire commons, the ontology will need to be augmented to accommodate the new semantic understanding. The ontology can also be visualized under “browse keywords” on the wifire commons front page.

YAGO (Yet Another Great Ontology) is an open source[3] knowledge base developed at the Max Planck Institute for Informatics in Saarbrücken. It is automatically extracted from Wikipedia and other sources.

As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities.[4] The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames.[5] The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts.[6] To integrate it to the linked data cloud, YAGO has been linked to the DBpedia ontology[7] and to the SUMO ontology.[8]

YAGO3 is provided in Turtle and tsv formats. Dumps of the whole database are available, as well as thematic and specialized dumps. It can also be queried through various online browsers and through a SPARQL endpoint hosted by OpenLink Software. The source code of YAGO3 is available on GitHub.

A description of the Environment Ontology (ENVO) is published in the Journal of Biomedical Semantics in an article by Buttigieg et al. and a paper describing its development until mid-2016 is available here

                 Our latest releases are described here

                 More information and guides for using ENVO in annotation exercises are available at www.environmentontology.org

                 Please note: ENVO is not an "authority" in itself, but we do try to provide a semantic/ontological expression of existing authoritative classifications alongside project-based or individual knowledge. We aim to create a FAIR compliant space where expressions of this knowledge can co-exist and interoperate.

FoodOn aims to be the Lingua Franca about human and companion animal food product vocabulary. As an open-source inter-agency supported project, it provides a neutral and ontology-driven standard for government agencies, industry, nonprofits and consumers to name and reference food products and their components throughout the food supply chain. In partnership with other ontologies, the project also explores the semantic bedrock of vocabulary involved in modelling food composition, food processing, nutrition and food related disease.

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