The following outline is provided as an overview of and topical guide to ontologies:

Ontology – type of knowledge base that includes the concepts of a subject and the relationships between those concepts. In a formal ontology, each concept is represented by its own unique term (that is, each term has only one meaning), to eliminate ambiguity and guessing. The relationships recorded in formal ontologies support making automatic inferences about the world and the things in it. Thus, formal ontologies are models of reality that computer programs can use for automated reasoning, and are an important part of reasoning systems (in artificial intelligence, or AI). By contrast, an informal or natural ontology is one written using natural language, and it is more difficult to write programs for processing one (but no less important). In the jargon of the field, an ontology is a linguistic knowledge representation structure of the types, parts, properties, and interrelationships of the entities for a particular domain of discourse. Ontologies are a way to describe subject areas using taxonomies and classification networks, essentially defining the structure of knowledge for various domains.

What type of thing is an ontology?

Ontologies can be described as all of the following:

• A type of tool of knowledge representation and reasoning (KR) – KR is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Examples of knowledge representation formalisms include semantic nets, frames, rules, and ontologies.

Types of ontologies

• Lightweight ontology
• Upper ontology – ontology which describes very general concepts that are the same across all knowledge domains.
  • Standard upper ontology – (IEEE P1600.1 term for a) near-universal upper ontology (or foundation ontology). Several upper ontologies are competing to become the standard.

Ontology components

Ontology components

• Individuals – instances or objects (the basic or "ground level" objects)
• Classes – sets, collections, concepts, types of objects, or kinds of things.^[1]
• Attributes – aspects, properties, features, characteristics, or parameters that objects (and classes) can have
• Relations – ways in which classes and individuals can be related to one another
• Function terms – complex structures formed from certain relations that can be used in place of an individual term in a statement
• Restrictions – formally stated descriptions of what must be true in order for some assertion to be accepted as input
• Rules – statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form
• Axioms – assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements.
• Events – the changing of attributes or relations
• Ontology notation – ontologies are commonly encoded using ontology languages.

Applications of ontologies

Linguistics applications

Reasoning applications

Search applications

Examples of ontologies

• BabelNet – very large multilingual semantic network and ontology, lexicalized in many languages
• BMO,^[2] – e-Business Model Ontology based on a review of enterprise ontologies and business model literature
• CContology (Customer Complaint Ontology)^[3] – e-business ontology to support online customer complaint management
• CIDOC Conceptual Reference Model – ontology for cultural heritage^[4]
• Dublin Core – simple ontology for documents and publishing
• Foundational, Core and Linguistic Ontologies^[5]
• Friend of a Friend – ontology for describing persons, their activities and their relations to other people and objects
• Gellish English dictionary – ontology that includes a dictionary and taxonomy that includes an upper ontology and a lower ontology that focusses on industrial and business applications in engineering, technology and procurement. See also Gellish as Open Source project on SourceForge.
• Geopolitical ontology – describes geopolitical information created by Food and Agriculture Organization(FAO). The geopolitical ontology includes names in multiple languages (English, French, Spanish, Arabic, Chinese, Russian and Italian); maps standard coding systems (UN, ISO, FAOSTAT, AGROVOC, etc.); provides relations among territories (land borders, group membership, etc.); and tracks historical changes. In addition, FAO provides web services <http://www.fao.org/countryprofiles/webservices.asp?lang=en> of geopolitical ontology and a module maker <http://www.fao.org/countryprofiles/geoinfo/modulemaker/index.html> to download modules of the geopolitical ontology into different formats (RDF, XML, and EXCEL). See more information on the FAO Country Profiles geopolitical ontology web page <http://www.fao.org/countryprofiles/geoinfo.asp?lang=en>.
• IDEAS Group^[6] – formal ontology for enterprise architecture being developed by the Australian, Canadian, UK and U.S. Defence Depts.
• Indiana Philosophy Ontology Project – model of the discipline of philosophy, made available https://inpho.cogs.indiana.edu online which makes it possible to search and navigate via relations among philosophical ideas, scholars, and their works.
• Linkbase^[7] – formal representation of the biomedical domain, founded upon Basic Formal Ontology.
• OMNIBUS Ontology^[8] – ontology of learning, instruction, and instructional design
• Ontology for Biomedical Investigations – open access, integrated ontology for the description of biological and clinical investigations
• PRO^[9] – Protein Ontology of the Protein Information Resource, Georgetown University
• Program abstraction taxonomy program abstraction taxonomy
• Protein Ontology^[10] – for proteomics
• ThoughtTreasure –
• UMBEL – lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc
• WikiTaxonomy – hierarchy of classes and instances automatically generated from Wikipedia's category system
• WordNet – lexical reference system
• YAGO (Yet Another Great Ontology) – knowledge base developed at the Max Planck Institute for Computer Science in Saarbrücken. It is automatically extracted from Wikipedia and other sources. It includes knowledge about more than 10 million entities and contains more than 120 million facts about these entities.

Examples of biological and biomedical ontologies

• Gene Ontology for genomics
• BioPAX^[11] – ontology for the exchange and interoperability of biological pathway (cellular processes) data
• CCO and GexKB^[12] – Application Ontologies (APO) that integrate diverse types of knowledge with the Cell Cycle Ontology (CCO) and the Gene Expression Knowledge Base (GexKB)
• Disease Ontology^[13] – ontology designed to facilitate the mapping of diseases and associated conditions to particular medical codes. It was originally developed at Northwestern University and is associated with the Open Biomedical Ontologies Foundry.
• Foundational Model of Anatomy^[14] – reference ontology for the domain of anatomy. It is a symbolic representation of the canonical, phenotypic structure of an organism; a spatial-structural ontology of anatomical entities and relations which form the physical organization of an organism at all salient levels of granularity.
• NCBO Bioportal,^[15] biological and biomedical ontologies and associated tools to search, browse and visualise
• NIFSTD Ontologies from the Neuroscience Information Framework: a modular set of ontologies for the neuroscience domain. See http://neuinfo.org
• OBO-Edit,^[16] an ontology browser for most of the Open Biological and Biomedical Ontologies
• OBO Foundry,^[17] a suite of interoperable reference ontologies in biology and biomedicine
• ONSTR,^[18] Ontology for Newborn Screening Follow-up and Translational Research [1], Newborn Screening Follow-up Data Integration Collaborative, Emory University, Atlanta, GA. See also https://nbsdc.org/projectmission.php
• Plant Ontology^[19] for plant structures and growth/development stages, etc.
• POPE, Purdue Ontology for Pharmaceutical Engineering
• SNOMED CT (Systematized Nomenclature of Medicine -- Clinical Terms)
• Systems Biology Ontology (SBO) – for computational models in biology
• SWEET^[20] – Semantic Web for Earth and Environmental Terminology
• TIME-ITEM, Topics for Indexing Medical Education
• Uberon^[21] – representing animal anatomical structures

Examples of upper ontologies

Upper ontology – ontology which describes very general concepts that are the same across all knowledge domains. Examples of upper ontologies include:

• Basic Formal Ontology,^[22] a formal upper ontology designed to support scientific research
• COSMO,^[23] a Foundation Ontology (current version in OWL) that is designed to contain representations of all of the primitive concepts needed to logically specify the meanings of any domain entity. It is intended to serve as a basic ontology that can be used to translate among the representations in other ontologies or databases. It started as a merger of the basic elements of the OpenCyc and SUMO ontologies, and has been supplemented with other ontology elements (types, relations) so as to include representations of all of the words in the Longman dictionary defining vocabulary.
• DOLCE, a Descriptive Ontology for Linguistic and Cognitive Engineering
• GOLD,^[24] General Ontology for Linguistic Description
• GUM (Generalized Upper Model),^[25] a linguistically motivated ontology for mediating between clients systems and natural language technology
• Suggested Upper Merged Ontology (SUMO) – formal upper ontology
• YAMATO,^[26] Yet Another More Advanced Top-level Ontology

History of ontologies

History of ontologies

Ontology languages

Ontology language – formal language used to construct ontologies, that allows the encoding of knowledge about specific domains. An ontology language may include reasoning rules that support the processing of that knowledge.

• RDF Schema (Resource Description Framework Schema) – set of classes with certain properties using the RDF extensible knowledge representation data model, providing basic elements for the description of ontologies, otherwise called RDF vocabularies, intended to structure RDF web resources.
• Web Ontology Language (OWL) – family of knowledge representation languages for authoring ontologies.

Ontology engineering

Ontology engineering – building ontologies, and the field that studies the methods and methodologies for building ontologies.

Ontology learning

Ontology learning –

Ontology organizations

Ontology publications

Persons influential in ontologies

• Adam Pease – American computer scientist doing research in ontology and formal reasoning. He is best known as the Technical Editor of the Suggested Upper Merged Ontology (SUMO) upper ontology intended as a foundation ontology for a variety of computer information processing systems.