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A knowledge base (KB) is fact-oriented but ontology is schema-oriented.
KB: In the Google KGraph, you have certainly a schema (like the one described by DBPedia, Freebase, etc.) and a set of facts (A relation B, "Paris isA City, Paris hasInhabitants 2M, Paris isCapitalOf France, France isA Country, etc.). So, we can (easily) answer to questions like "Number of inhabitants in Paris?" or you can provide a short description of Paris, France (or any given entity in general).
The domain ontology tells people which are the main concepts of a domain, how are these concepts related and which attributes do they have. Here the focus is on the description, with the highest possible expressiveness (disjointness, values restrictions, cardinality restrictions) and the useful annotations (synonyms, definition of terms, examples, comments on design choices, etc.), of the entities of a given domain. Data (or facts) are not the main concern when designing a domain ontology VS KB.
This draft specification provides a collection of fields related to the contextual data of a specimen, its genomic sequencing, and its pathogenic epidemiology.
FoodOn belongs to the open source OBO Foundry consortium of interoperable life science oriented ontologies and consequently supports FAIR data annotation and sharing objectives across a wide variety of academic research, government and commercial sectors.
FoodOn is available at FoodOn GitHub in the main foodon. owl file and imports/ folder files, with development work carried on in the src/ ontology/ subfolder. The LanguaL conversion script is located in the /src/ontology/imports/langual folder.
FoodOn currently draws upon 16 OBO Foundry ontologies, culminating in over 27,000 classes. Creation and revision of LanguaL facet term logical definitions is ongoing. FoodOn is being validated against the Enterobase pathogenic sequence database and GenomeTrackr sample descriptions. FoodOn provides ingredients for the recent Ontology for Nutritional Studies (ONS) under development for the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI), and is also part of a draft foodborne pathogen sequence repository standard.
FoodOn reuses terms from OBO Foundry ontologies such as environmental terms from ENVO, agriculture terms from AGRO, plant and animal anatomy terms from UBERON, and PO, organisms from NCBITaxon, relations from RO, and nutritional components from CDNO.
Conversely, FoodOn terms are reused in a growing list of ontologies such as ENVO, CDNO, ONE, ONS, FIDEO, FOBI, ECTO, and DOID.
One problem concerning multilingual thesauri is the multiplicity of natural languages: corresponding terms of different languages are not always semantically equivalent. It was chosen to render LanguaL™ language-independent, to be used in the USA and Europe for numeric data banks on food composition (nutrients and contaminants), food consumption and legislation. Each descriptor is identified by a unique code pointing to equivalent terms in different languages (e.g. Czech, Danish, English, French, German, Italian, Portuguese, Spanish and Hungarian). LanguaL™ thus facilitates links to many different food data banks and contributes to coherent data exchange. LanguaL™ is in common use for describing, capturing and retrieving data about food, adapted to computerised national and international food composition and consumption databanks.
For example, in North America “biscuit” refers to a softer “quick bread” (FOODON_03301884), while in Britain it usually means a hard, flat unleavened baked product (FOODON_00002466). Some North American cookies (FOODON_03301585) fall under the British biscuit category too. FoodOn helps resolve such confusion by providing ontology identifiers that yield terms with disambiguat- ing product descriptions.
To jumpstart this broader ambition, FoodOn has drawn many of its initial terms from LanguaL, a library science and ontology friendly food classification system consisting of 14 food product description facets including plant or animal food source, chemical additive, preservation or cooking process, packaging, and standard national and international upper-level product type schemes.
FoodOn now has coverage of some Asian foods via GitHub requests; other databases (like the LanguaL-indexed French, Greek, and Hungarian ones) could be imported in the future to increase international coverage.
FoodOn supports reuse in third-party standards via its GitHub repository, allowing users to access and retrieve a particular version or release at any time. However to incorporate such ontology content into agency infrastructure directly often requires a mastery of fairly complex Semantic Web Technology, including knowledge of OWL and the associated SPARQL querying language, as well as the abstractions of an upper-level ontology under which terms are organized.
Technically this is accomplished using a python script that uses the rdflib module to read an ontology into memory as an RDF graph of triples, and then uses SPARQL to query it and convert it into a JSON representation which the GEEM web interface then renders as HTML forms or downloadable specifications.
An open-source, multilingual food vocabulary of basic ingredients and directly derived food products can form the base lingua franca that product research and development efforts reference in proprietary rules or machine learning algorithms that drive food-related software.
LanguaL™ - the International Framework for Food Description. LanguaL™ is a Food Description Thesaurus. LanguaL™ stands for "Langua aLimentaria" or "language of food". It is an automated method for describing, capturing and retrieving data about food. The work on LanguaL™ was started in the late 1970's by the Center for Food Safety and Applied Nutrition (CFSAN) of the United States Food and Drug Administration as an ongoing co-operative effort of specialists in food technology, information science and nutrition.
LanguaL™ is a multilingual thesaural system using facetted classification. Each food is described by a set of standard, controlled terms chosen from facets characteristic of the nutritional and/or hygienic quality of a food, as for example the biological origin, the methods of cooking and conservation, and technological treatments.
Multiple component foods are more challenging because LanguaL itself does not aspire to be a global food type catalog, and so provides no facility for giving identifiers to component food products. LanguaL suggests curators follow a “Full Ingredient Indexing” protocol in which all ingredients of a product are coded in descending order by weight, but for products like lasagna, one cannot reference components like “lasagna noodle” or “cheese” in the list—only food source items like “durum wheat” are allowed. LanguaL provides one other way to reference other raw ingredients (besides the primary one) by a set of “ingredient added” terms which from an ontology perspective awkwardly duplicate some but not all terms in the “food source” facet.
The mission of the OBO Foundry is to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate. To achieve this, OBO Foundry participants follow and contribute to the development of an evolving set of principles including open use, collaborative development, non-overlapping and strictly-scoped content, and common syntax and relations, based on ontology models that work well, such as the Gene Ontology (GO).
The W3C Web Ontology Language (OWL) is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that knowledge expressed in OWL can be exploited by computer programs, e.g., to verify the consistency of that knowledge or to make implicit knowledge explicit. OWL documents, known as ontologies, can be published in the World Wide Web and may refer to or be referred from other OWL ontologies. OWL is part of the W3C’s Semantic Web technology stack, which includes RDF, RDFS, SPARQL, etc.
The Food Ontology is a simple lightweight ontology for publishing data about recipes, including the foods they are made from and the foods they create as well as the diets, menus, seasons, courses and occasions they may be suitable for. Whilst it originates in a specific BBC use case, the Food Ontology should be applicable to a wide range of recipe data publishing across the web.
The Food Ontology sits alongside existing work such as Google's Rich Snippets for Recipes. While Google, and schema.org, provide a way to represent literal strings in a structured way the Food Ontology provides a richly linked model that more completely describes the recipe and its context. Food Ontology, Google Rich Snippets and Schema.org microdata for recipes are all able to co-exist peacefully within the same site.
List of all food-related ontologies
BioPortal - the world's most comprehensive repository of biomedical ontologies
Czech Food Composition Database- This application presents data on the level of a compiled database reporting a single value for each food / component combination. Data have been processed and documented in accordance with the standardized procedure of the international net of excellence EuroFIR (European Food Information Resource). Releases data for 934 foods.
Relation Ontology - Relationship types shared across multiple ontologies.
Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond.
Simple Knowledge Organization System (SKOS) and associated web technologies aim to enable preexisting controlled vocabularies to be consumed on the web and to allow vocabulary creators to publish born-digital vocabularies on the web.
This guide allows catalogers, librarians, and other information professionals to understand and use SKOS, a World Wide Web Consortium (W3C) standard designed for the representation of controlled vocabularies, to be consumed within the web environment.|
An example of a comprehensive Semantic Middleware that can integrate with various graph databases. Also, they have a lot of articles, webinars, etc. on their website.