Ontology (information science)

(Redirected from Ontology (computer science))
Jump to: navigation, search

In computer science and information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really exist in a particular domain of discourse.

An ontology (in information science) compartmentalizes the variables needed for some set of computations and establishes the relationships between them.[1][2]

The fields of artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture all create ontologies to limit complexity and organize information. The ontology can then be applied to problem solving.

The knowledge density of a knowledge graph is the average number of attributes and binary relations issued from a given entity, and is commonly measured in facts per entity.[3]

Etymology and definition

The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek ὤν, ὄντος, ("being", "that which is"), present participle of the verb εἰμί ("be"). The core meaning within computer science is a model for describing the world that consists of a set of types, properties, and relationship types. There is also generally an expectation that the features of the model in an ontology should closely resemble the real world (related to the object).[4]


What ontologies have in common in both computer science and philosophy is the representation of entities, ideas and events, along with their properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontological relativity (e.g., Quine and Kripke in philosophy, Sowa and Guarino in computer science),[5] and debates about whether a normative ontology is viable (e.g., debates about foundationalism in philosophy, and the Cyc project in AI). Differences between the two are largely matters of focus. Computer scientists are more concerned with establishing fixed, controlled vocabularies, while philosophers are more concerned with first principles, such as whether there are such things as fixed essences or whether enduring objects must be ontologically more primary than processes.

Other fields make ontological assumptions that are sometimes explicitly elaborated and explored. For instance, the definition and ontology of economics (also sometimes called the political economy) is hotly debated especially in Marxist economics[6] where it is a primary concern, but also in other subfields.[7] Such concerns intersect with those of information science when a simulation or model is intended to enable economic decisions, for example to determine what capital assets are at risk and by how much (see risk management). Some claim that all social sciences have explicit ontology issues because they do not have hard falsifiability criteria like most models in physical sciences, and that indeed the lack of such widely accepted hard falsification criteria is what defines a social or soft science.[citation needed]


Historically, ontologies arise out of the branch of philosophy known as metaphysics, which deals with the nature of reality – of what exists. This fundamental branch is concerned with analyzing various types or modes of existence, often with special attention to the relations between particulars and universals, between intrinsic and extrinsic properties, and between essence and existence. The traditional goal of ontological inquiry in particular is to divide the world "at its joints" to discover those fundamental categories or kinds into which the world’s objects naturally fall.[8]

During the second half of the 20th century, philosophers extensively debated the possible methods or approaches to building ontologies without actually building any very elaborate ontologies themselves.[citation needed] By contrast, computer scientists were building some large and robust ontologies, such as WordNet and Cyc, with comparatively little debate over how they were built.[citation needed]

Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that capturing knowledge is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge systems. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy.[9]

In the early 1990s, the widely cited Web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber[10] is credited with a deliberate definition of ontology as a technical term in computer science. Gruber introduced the term to mean a specification of a conceptualization:

An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy.[11]

According to Gruber (1993):

Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions — that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world.[12] To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms.[1]

As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity."[13]


Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. As mentioned above, most ontologies describe individuals (instances), classes (concepts), attributes, and relations. In this section each of these components is discussed in turn.

Common components of ontologies include:

Instances or objects (the basic or "ground level" objects)
Sets, collections, concepts, classes in programming, types of objects, or kinds of things
Aspects, properties, features, characteristics, or parameters that objects (and classes) can have
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
Formally stated descriptions of what must be true in order for some assertion to be accepted as input
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
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 those disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements
The changing of attributes or relations

Ontologies are commonly encoded using ontology languages.


Domain ontology

A domain ontology (or domain-specific ontology) represents concepts which belong to part of the world. Particular meanings of terms applied to that domain are provided by domain ontology. For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings.

Since domain ontologies represent concepts in very specific and often eclectic ways, they are often incompatible. As systems that rely on domain ontologies expand, they often need to merge domain ontologies into a more general representation. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.).

At present, merging ontologies that are not developed from a common foundation ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same foundation ontology to provide a set of basic elements with which to specify the meanings of the domain ontology elements can be merged automatically. There are studies on generalized techniques for merging ontologies,[14] but this area of research is still largely theoretical.

Upper ontology

An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain sets.

Standardized upper ontologies available for use include BFO, BORO method, Dublin Core, GFO, OpenCyc/ResearchCyc, SUMO, UMBEL, the Unified Foundational Ontology (UFO),[15] and DOLCE.[16][17] WordNet is not an ontology but has been considered an upper ontology by some and has been used as a linguistic tool for learning domain ontologies.[18]

Hybrid ontology

The Gellish ontology is an example of a combination of an upper and a domain ontology.


A survey of ontology visualization techniques is presented by Katifori et al.[19] The most established ontology visualization techniques, namely indented tree and graph are evaluated by Fu et al.[20] A visual language for ontologies represented in OWL is specified by the Visual Notation for OWL Ontologies (VOWL).[21]


Ontology engineering (also called ontology building) is a set of tasks related to the development of ontologies for a particular domain.[22] It is a subfield of knowledge engineering that studies the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tools and languages that support them.[23][24]

Ontology engineering aims to make explicit the knowledge contained in software applications, and organizational procedures for a particular domain. Ontology engineering offers a direction for overcoming semantic obstacles, such as those related to the definitions of business terms and software classes. Known challenges with ontology engineering include:

  1. Ensuring the ontology is current with domain knowledge and term use
  2. Providing sufficient specificity and concept coverage for the domain of interest, thus minimizing the content completeness problem
  3. Ensuring the ontology can support its use cases


Ontology editors are applications designed to assist in the creation or manipulation of ontologies. They often express ontologies in one of many ontology languages. Some provide export to other ontology languages however.

Among the most relevant criteria for choosing an ontology editor are the degree to which the editor abstracts from the actual ontology representation language used for persistence and the visual navigation possibilities within the knowledge model. Next come built-in inference engines and information extraction facilities, and the support of meta-ontologies such as OWL-S, Dublin Core, etc. Another important feature is the ability to import & export foreign knowledge representation languages for ontology matching. Ontologies are developed for a specific purpose and application.

  • a.k.a. software (Ontology, taxonomy and thesaurus management software available from The Synercon Group)
  • Anzo for Excel (Includes an RDFS and OWL ontology editor within Excel; generates ontologies from Excel spreadsheets)
  • Be Informed Suite (Commercial tool for building large ontology based applications. Includes visual editors, inference engines, export to standard formats)
  • Chimaera (Other web service by Stanford)
  • CmapTools Ontology Editor (COE) (Java based ontology editor from the Florida Institute for Human and Machine Cognition. Supports numerous formats)
  • dot15926 Editor (Open source ontology editor for data compliant to engineering ontology standard ISO 15926. Allows Python scripting and pattern-based data analysis. Supports extensions.)
  • EMFText OWL2 Manchester Editor, Eclipse-based, open-source, Pellet integration
  • Enterprise Architect, along with UML modeling, supports OMG's Ontology Definition MetaModel which includes OWL and RDF.
  • Fluent Editor, a comprehensive ontology editor for OWL and SWRL with Controlled Natural Language (Controlled English). Supports OWL, RDF, DL and Functional rendering, unlimited imports and built-in reasoning services.
  • HOZO (Java-based graphical editor especially created to produce heavy-weight and well thought out ontologies, from Osaka University and Enegate Co, ltd.)
  • Java Ontology Editor (JOE) (1998)
  • KAON (single user and server based solutions possible, open source, from FZI/AIFB Karlsruhe)
  • KMgen (Ontology editor for the KM language. km: The Knowledge Machine)
  • Knoodl (Free web application/service that is an ontology editor, wiki, and ontology registry. Supports creation of communities where members can collaboratively import, create, discuss, document and publish ontologies. Supports OWL, RDF, RDFS, and SPARQL queries. Available since early Nov 2006 from Revelytix, Inc..)
  • Menthor Editor (An ontology engineering tool for dealing with OntoUML. It also includes OntoUML syntax validation, Alloy simulation, Anti-Pattern verification, and transformations from OntoUML to OWL, SBVR and Natural Language (Brazilian Portuguese))
  • Model Futures IDEAS AddIn (free) A plug-in for Sparx Systems Enterprise Architect that allows IDEAS Group 4D ontologies to be developed using a UML profile
  • Model Futures OWL Editor (Free) Able to work with very large OWL files (e.g. Cyc) and has extensive import and export capabilities (inc. UML, Thesaurus Descriptor, MS Word, CA ERwin Data Modeler, CSV, etc.)
  • myWeb (Java-based, mySQL connection, bundled with applet that allows online browsing of ontologies (including OBO))
  • Neologism (Web-based, open source, supports RDFS and a subset of OWL, built on Drupal)
  • NeOn Toolkit (Eclipse-based, open source, OWL support, several import mechanisms, support for reuse and management of networked ontologies, visualization, etc.…from NeOn Project)
  • OBIS (Web based user interface that allows to input ontology instances in a user friendly way that can be accessed via SPARQL endpoint)
  • OBO-Edit (Java-based, downloadable, open source, developed by the Gene Ontology Consortium for editing biological ontologies)
  • OntoStudio (Eclipse-based, downloadable, support for RDF(S), OWL and ObjectLogic (derived from F-Logic), graphical rule editor, visualizations, from semafora systems)
  • Ontolingua (Web service offered by Stanford University)
  • ONTOLIS (Collaborative web application for managing ontologies and knowledge engineering, web-browser-based graphical rules editor, sophisticated search and export interface. Web service available to link ontology information to existing data)
  • Open Semantic Framework (OSF), an integrated software stack using semantic technologies for knowledge management, which includes an ontology editor
  • OWLGrEd (A graphical ontology editor, easy-to-use)
  • PoolParty Thesaurus Server (Commercial ontology, taxonomy and thesaurus management software available from Semantic Web Company, fully based on standards like RDFS, SKOS and SPARQL, integrated with Virtuoso Universal Server)
  • Protégé (Java-based, downloadable, Supports OWL, open source, many sample ontologies, from Stanford University)
  • ScholOnto (net-centric representations of research)
  • Semantic Turkey (Firefox extension - also based on Java - for managing ontologies and acquiring new knowledge from the Web; developed at University of Rome, Tor Vergata )
  • Sigma knowledge engineering environment is a system primarily for development of the Suggested Upper Merged Ontology
  • Swoop (Java-based, downloadable, open source, OWL Ontology browser and editor from the University of Maryland)
  • Semaphore Ontology Manager (Commercial ontology, taxonomy and thesaurus management software available from Smartlogic Semaphore Limited. Intuitive tool to manage the entire "build - enhance - review - maintain" ontology lifecycle.)
  • Synaptica (Ontology, taxonomy and thesaurus management software available from Synaptica, LLC. Web based, supports OWL and SKOS.)
  • TopBraid Composer (Eclipse-based, downloadable, full support for RDFS and OWL, built-in inference engine, SWRL editor and SPARQL queries, visualization, import of XML and UML, from TopQuadrant)
  • Transinsight (The editor is especially designed for creating text mining ontologies and part of GoPubMed.org)
  • WebODE (Web service offered by the Technical University of Madrid)
  • TwoUse Toolkit (Eclipse-based, open source, model-driven ontology editing environment especially designed for software engineers)
  • Thesaurus Master (Manages creation and use of ontologies for use in data management and semantic enrichment by enterprise, government, and scholarly publishers.)
  • TODE (A Dot Net-based Tool for Ontology Development and Editing)
  • VocBench (Collaborative Web Application for SKOS/SKOS-XL Thesauri Management - developed on a joint effort between University of Rome, Tor Vergata and the Food and Agriculture Organization of the United Nations: FAO )


Ontology learning is the automatic or semi-automatic creation of ontologies, including extracting a domain's terms from natural language text. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process. Information extraction and text mining have been explored to automatically link ontologies to documents, for example in the context of the BioCreative challenges.[25]


An ontology language is a formal language used to encode an ontology. There are a number of such languages for ontologies, both proprietary and standards-based:

  • Common Algebraic Specification Language is a general logic-based specification language developed within the IFIP working group 1.3 "Foundations of System Specifications" and is a de facto standard language for software specifications. It is now being applied to ontology specifications in order to provide modularity and structuring mechanisms.
  • Common logic is ISO standard 24707, a specification of a family of ontology languages that can be accurately translated into each other.
  • The Cyc project has its own ontology language called CycL, based on first-order predicate calculus with some higher-order extensions.
  • DOGMA (Developing Ontology-Grounded Methods and Applications) adopts the fact-oriented modeling approach to provide a higher level of semantic stability.
  • The Gellish language includes rules for its own extension and thus integrates an ontology with an ontology language.
  • IDEF5 is a software engineering method to develop and maintain usable, accurate, domain ontologies.
  • KIF is a syntax for first-order logic that is based on S-expressions. SUO-KIF is a derivative version supporting the Suggested Upper Merged Ontology.
  • MOF and UML are standards of the OMG
  • Olog is a category theoretic approach to ontologies, emphasizing translations between ontologies using functors.
  • OBO, a language used for biological and biomedical ontologies.
  • OntoUML is an ontologically well-founded profile of UML for conceptual modeling of domain ontologies.
  • OWL is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML, and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs.
  • Rule Interchange Format (RIF) and F-Logic combine ontologies and rules.
  • Semantic Application Design Language (SADL)[26] captures a subset of the expressiveness of OWL, using an English-like language entered via an Eclipse Plug-in.
  • SBVR (Semantics of Business Vocabularies and Rules) is an OMG standard adopted in industry to build ontologies.
  • TOVE Project, TOronto Virtual Enterprise project

Published examples

  • AURUM - Information Security Ontology,[27] An ontology for information security knowledge sharing, enabling users to collaboratively understand and extend the domain knowledge body. It may serve as a basis for automated information security risk and compliance management.
  • BabelNet, a very large multilingual semantic network and ontology, lexicalized in many languages
  • Basic Formal Ontology,[28] a formal upper ontology designed to support scientific research
  • BioPAX,[29] an ontology for the exchange and interoperability of biological pathway (cellular processes) data
  • BMO,[30] an e-Business Model Ontology based on a review of enterprise ontologies and business model literature
  • SSBMO,[31] a Strongly Sustainable Business Model Ontology based on a review of the systems based natural and social science literature (including business). Includes critique of and significant extensions to the Business Model Ontology (BMO).
  • CCO and GexKB,[32] Application Ontologies (APO) that integrate diverse types of knowledge with the Cell Cycle Ontology (CCO) and the Gene Expression Knowledge Base (GexKB)
  • CContology (Customer Complaint Ontology),[33] an e-business ontology to support online customer complaint management
  • CIDOC Conceptual Reference Model, an ontology for cultural heritage[34]
  • COSMO,[35] 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.
  • Cyc, a large Foundation Ontology for formal representation of the universe of discourse
  • Disease Ontology,[36] designed to facilitate the mapping of diseases and associated conditions to particular medical codes
  • DOLCE, a Descriptive Ontology for Linguistic and Cognitive Engineering[16][17]
  • Drammar, ontology of drama
  • Dublin Core, a simple ontology for documents and publishing
  • Financial Industry Business Ontology (FIBO), a business conceptual ontology for the financial industry[37]
  • Foundational, Core and Linguistic Ontologies[38]
  • Foundational Model of Anatomy,[39] an ontology for human anatomy
  • Friend of a Friend, an ontology for describing persons, their activities and their relations to other people and objects
  • Gene Ontology for genomics
  • Gellish English dictionary, an 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.
  • Geopolitical ontology, an ontology describing 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 of geopolitical ontology and a module maker to download modules of the geopolitical ontology into different formats (RDF, XML, and EXCEL). See more information at FAO Country Profiles.
  • GAO (General Automotive Ontology) - an ontology for the automotive industry that includes 'car' extensions [40]
  • GOLD,[41] General Ontology for Linguistic Description
  • GUM (Generalized Upper Model),[42] a linguistically motivated ontology for mediating between clients systems and natural language technology
  • IDEAS Group,[43] a formal ontology for enterprise architecture being developed by the Australian, Canadian, UK and U.S. Defence Depts.
  • Linkbase,[44] a formal representation of the biomedical domain, founded upon Basic Formal Ontology.
  • LPL, Lawson Pattern Language
  • NCBO Bioportal,[45] 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.
  • OBO-Edit,[46] an ontology browser for most of the Open Biological and Biomedical Ontologies
  • OBO Foundry,[47] a suite of interoperable reference ontologies in biology and biomedicine
  • OMNIBUS Ontology,[48] an ontology of learning, instruction, and instructional design
  • Ontology for Biomedical Investigations, an open-access, integrated ontology of biological and clinical investigations
  • ONSTR,[49] Ontology for Newborn Screening Follow-up and Translational Research, Newborn Screening Follow-up Data Integration Collaborative, Emory University, Atlanta.
  • Plant Ontology[50] for plant structures and growth/development stages, etc.
  • POPE, Purdue Ontology for Pharmaceutical Engineering
  • PRO,[51] the Protein Ontology of the Protein Information Resource, Georgetown University
  • ProbOnto, knowledge base and ontology of probability distributions.[52][53]
  • Program abstraction taxonomy
  • Protein Ontology[54] for proteomics
  • RXNO Ontology, for name reactions in chemistry
  • Sequence Ontology,[55] for representing genomic feature types found on biological sequences
  • SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms)
  • Suggested Upper Merged Ontology, a formal upper ontology
  • Systems Biology Ontology (SBO), for computational models in biology
  • SWEET,[56] Semantic Web for Earth and Environmental Terminology
  • ThoughtTreasure ontology
  • TIME-ITEM, Topics for Indexing Medical Education
  • Uberon,[57] representing animal anatomical structures
  • UMBEL, a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc
  • WordNet, a lexical reference system
  • YAMATO,[58] Yet Another More Advanced Top-level Ontology

The W3C Linking Open Data community project coordinates attempts to converge different ontologies into worldwide Semantic Web.


The development of ontologies for the Web has led to the emergence of services providing lists or directories of ontologies with search facility. Such directories have been called ontology libraries.

The following are libraries of human-selected ontologies.

  • COLORE[59] is an open repository of first-order ontologies in Common Logic with formal links between ontologies in the repository.
  • DAML Ontology Library[60] maintains a legacy of ontologies in DAML.
  • Ontology Design Patterns portal[61] is a wiki repository of reusable components and practices for ontology design, and also maintains a list of exemplary ontologies.
  • Protégé Ontology Library[62] contains a set of OWL, Frame-based and other format ontologies.
  • SchemaWeb[63] is a directory of RDF schemata expressed in RDFS, OWL and DAML+OIL.

The following are both directories and search engines. They include crawlers searching the Web for well-formed ontologies.

  • OBO Foundry is a suite of interoperable reference ontologies in biology and biomedicine.[64][65]
  • Bioportal (ontology repository of NCBO)
  • OntoSelect[66] Ontology Library offers similar services for RDF/S, DAML and OWL ontologies.
  • Ontaria[67] is a "searchable and browsable directory of semantic web data" with a focus on RDF vocabularies with OWL ontologies. (NB Project "on hold" since 2004).
  • Swoogle is a directory and search engine for all RDF resources available on the Web, including ontologies.
  • Open Ontology Repository initiative
  • ROMULUS is a foundational ontology repository aimed at improving semantic interoperability. Currently there are three foundational ontologies in the repository: DOLCE, BFO and GFO.

Examples of applications

In general, ontologies can be used beneficially in several fields.

See also

Related philosophical concepts


  1. ^ a b Gruber, Thomas R. (June 1993). "A translation approach to portable ontology specifications" (PDF). Knowledge Acquisition. 5 (2): 199–220. doi:10.1006/knac.1993.1008. 
  2. ^ Arvidsson, F.; Flycht-Eriksson, A. "Ontologies I" (PDF). Retrieved 26 November 2008. 
  3. ^ Hegde, Manjunath (2015). "An Entity-centric Approach for Overcoming Knowledge Graph Sparsity" (PDF). talukdar.net. 
  4. ^ Garshol, L. M. (2004). "Metadata? Thesauri? Taxonomies? Topic Maps! Making sense of it all" (PDF). Retrieved 2017-12-03. 
  5. ^ Sowa, J. F. (1995). "Top-level ontological categories". International Journal of Human-Computer Studies. 43 (5-6 (November/December)): 669–85. doi:10.1006/ijhc.1995.1068. 
  6. ^ Palermo, Giulio (10 January 2007). "The ontology of economic power in capitalism: mainstream economics and Marx". Cambridge Journal of Economics. 31 (4): 539–561. doi:10.1093/cje/bel036. Retrieved 16 June 2013 – via Oxford Journals. 
  7. ^ Zuniga, Gloria L. (1999-02-02). "An Ontology Of Economic Objects". Ideas. Research Division of the Federal Reserve Bank of St. Louis. Retrieved 2013-06-16. 
  8. ^ Benjamin, Perakath C.; Menzel, Christopher P.; Mayer, Richard J.; Fillion, Florence; Futrell, Michael T.; deWitte, Paula S.; Lingineni, Madhavi (September 21, 1994). "IDEF5 Method Report" (PDF). Knowledge Based Systems, Inc. 
  9. ^ Gruber, T. (2008). Liu, Ling; Özsu, M. Tamer, eds. Ontology. Encyclopedia of Database Systems. Springer-Verlag. ISBN 978-0-387-49616-0. 
  10. ^ Gruber, T. (1995). "Toward Principles for the Design of Ontologies Used for Knowledge Sharing". International Journal of Human-Computer Studies. 43 (5–6): 907–928. doi:10.1006/ijhc.1995.1081. 
  11. ^ Gruber, T. (2001). "What is an Ontology?". Stanford University. Retrieved 2009-11-09. 
  12. ^ Enderton, H. B. (1972-05-12). A Mathematical Introduction to Logic (1 ed.). San Diego, CA: Academic Press. p. 295. ISBN 978-0-12-238450-9 2nd edition; January 5, 2001, ISBN 978-0-12-238452-3 
  13. ^ Feilmayr, Christina; Wöß, Wolfram (2016). "An analysis of ontologies and their success factors for application to business". Data & Knowledge Engineering: 1–23. Retrieved 23 May 2017. 
  14. ^ "Project: Dynamic Ontology Repair". University of Edinburgh Department of Informatics. Retrieved 2 January 2012. 
  15. ^ Giancarlo Guizzardi & Gerd Wagner. "A Unified Foundational Ontology and some Applications of it in Business Modeling" (PDF). Retrieved 31 March 2014. 
  16. ^ a b "Laboratory for Applied Ontology - DOLCE". Laboratory for Applied Ontology (LOA). Retrieved 10 February 2011. 
  17. ^ a b "OWL version of DOLCE+DnS". Semantic Technology Lab. Retrieved 21 February 2013. 
  18. ^ Navigli, Roberto; Velardi, Paola (2004). "Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites" (PDF). Computational Linguistics. MIT Press. 30 (2): 151–179. doi:10.1162/089120104323093276. 
  19. ^ Katifori, A.; Halatsis, C.; Lepouras, G.; Vassilakis, C.; Giannopoulou, E. (2007). "Ontology Visualization Methods - A Survey" (PDF). ACM Computing Surveys. 39 (4): 10. doi:10.1145/1287620.1287621. Archived from the original (PDF) on 4 March 2016. 
  20. ^ Fu, Bo; Noy, Natalya F.; Storey, Margaret-Anne (2013). "Indented Tree or Graph? A Usability Study of Ontology Visualization Techniques in the Context of Class Mapping Evaluation". The Semantic Web – ISWC 2013: 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21–25, 2013, Proceedings, Part I. Lecture Notes in Computer Science. 8218. Berlin: Springer. pp. 117–134. doi:10.1007/978-3-642-41335-3_8. ISBN 978-3-642-41335-3 – via SpringerLink. 
  21. ^ Negru, Stefan; Lohmann, Steffen; Haag, Florian (7 April 2014). "VOWL: Visual Notation for OWL Ontologies: Specification of Version 2.0". Visual Data Web. 
  22. ^ Pouchard, Line; Ivezic, Nenad; Schlenoff, Craig (March 2000). "Ontology Engineering for Distributed Collaboration in Manufacturing" (PDF). Proceedings of the AIS2000 conference. 
  23. ^ Gómez-Pérez, Ascunion; Fernández-López, Mariano; Corcho, Oscar (2004). Ontological Engineering: With Examples from the Areas of Knowledge Management, E-commerce and the Semantic Web (1 ed.). Springer. p. 403. ISBN 978-1-85233-551-9. 
  24. ^ De Nicola, Antonio; Missikoff, Michele; Navigli, Roberto (2009). "A Software Engineering Approach to Ontology Building" (PDF). Information Systems. Elsevier. 34 (2): 258–275. doi:10.1016/j.is.2008.07.002. 
  25. ^ Krallinger, M; Leitner, F; Vazquez, M; Salgado, D; Marcelle, C; Tyers, M; Valencia, A; Chatr-Aryamontri, A (2012). "How to link ontologies and protein-protein interactions to literature: Text-mining approaches and the Bio Creative experience". Database. 2012: bas017. doi:10.1093/database/bas017. PMC 3309177Freely accessible. PMID 22438567. 
  26. ^ "SADL". Sourceforge. Retrieved 10 February 2011. 
  27. ^ "AURUM - Information Security Ontology". Retrieved 29 January 2016. 
  28. ^ "Basic Formal Ontology (BFO)". Institute for Formal Ontology and Medical Information Science (IFOMIS). 
  29. ^ "BioPAX". Retrieved 10 February 2011. 
  30. ^ Osterwalder, Alexander; Pigneur, Yves (June 17–19, 2002). "An e-Business Model Ontology for Modeling e-Business" (PDF). 15th Bled eConference, Slovenia. 
  31. ^ Upward, Antony; Jones, Peter. "An Ontology for Strongly Sustainable Business Models: Defining an Enterprise Framework Compatible with Natural and Social Science". Organization & Environment. 29 (1): 97–123. doi:10.1177/1086026615592933. 
  32. ^ "About CCO and GexKB". Semantic Systems Biology. 
  33. ^ "CContology". Retrieved 10 February 2011. 
  34. ^ "The CIDOC Conceptual Reference Model (CRM)". Retrieved 10 February 2011. 
  35. ^ "COSMO". MICRA Inc. Retrieved 10 February 2011. 
  36. ^ Osborne, JD; Flatow, J; Holko, M; Lin, SM; Kibbe, WA; Zhu, LJ; Danila, MI; Feng, G; Chisholm, RL. "Annotating the human genome with Disease Ontology". BMC Genomics. 10 Suppl 1: S6. doi:10.1186/1471-2164-10-S1-S6. PMC 2709267Freely accessible. PMID 19594883. 
  37. ^ "Financial Industry Business Ontology (FIBO)". Retrieved 15 March 2017. 
  38. ^ "Foundational, Core and Linguistic Ontologies". Retrieved 10 February 2011. 
  39. ^ "Foundational Model of Anatomy". Retrieved 10 February 2011. 
  40. ^ "Car Extension". Retrieved 15 June 2017. 
  41. ^ "GOLD". Retrieved 10 February 2011. 
  42. ^ "Generalized Upper Model". Retrieved 10 February 2011. 
  43. ^ "The IDEAS Group Website". Retrieved 10 February 2011. 
  44. ^ "Linkbase". Archived from the original on 18 September 2008. Retrieved 10 February 2011. 
  45. ^ "Bioportal". National Center for Biological Ontology (NCBO). 
  46. ^ "Ontology browser for most of the Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP). 
  47. ^ "The Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP). 
  48. ^ "OMNIBUS Ontology". Retrieved 10 February 2011. 
  49. ^ "ONSTR". Retrieved 16 April 2014. 
  50. ^ "Plant Ontology". Retrieved 10 February 2011. 
  51. ^ "PRO". Retrieved 10 February 2011. 
  52. ^ "ProbOnto". Retrieved 1 July 2017. 
  53. ^ Swat, MJ; Grenon, P; Wimalaratne, S (2016). "ProbOnto: ontology and knowledge base of probability distributions". Bioinformatics. 32: 2719. doi:10.1093/bioinformatics/btw170. PMC 5013898Freely accessible. PMID 27153608. 
  54. ^ "Protein Ontology". Retrieved 10 February 2011. 
  55. ^ Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M (2005). "The Sequence Ontology: a tool for the unification of genome annotations". Genome Biology. 6 (5): R44. doi:10.1186/gb-2005-6-5-r44. PMC 1175956Freely accessible. PMID 15892872. 
  56. ^ "SWEET". Retrieved 10 February 2011. 
  57. ^ Mungall, CJ; Torniai, C; Gkoutos, GV; Lewis, SE; Haendel, MA (2012). "Uberon, an integrative multi-species anatomy ontology". Genome Biol. 13: R5. doi:10.1186/gb-2012-13-1-r5. PMC 3334586Freely accessible. PMID 22293552. 
  58. ^ "YAMATO". Retrieved 10 February 2011. 
  59. ^ "COLORE". Retrieved 4 May 2011. 
  60. ^ "DAML Ontology Library". Retrieved 10 February 2011. 
  61. ^ "ODP Library". Retrieved 21 February 2013. 
  62. ^ "Protege Ontology Library". Retrieved 10 February 2011. 
  63. ^ "SchemaWeb". Archived from the original on 10 August 2011. Retrieved 10 February 2011. 
  64. ^ "OBO Foundry". Retrieved 10 February 2011. 
  65. ^ Smith, B.; Ashburner, M.; Rosse, C.; Bard, J.; Bug, W.; Ceusters, W.; Goldberg, L. J.; Eilbeck, K.; Ireland, A.; Mungall, C. J.; Leontis, N.; Rocca-Serra, P.; Ruttenberg, A.; Sansone, S. A.; Scheuermann, R. H.; Shah, N.; Whetzel, P. L.; Lewis, S. (2007). "The OBO Foundry: Coordinated evolution of ontologies to support biomedical data integration". Nature Biotechnology. 25 (11): 1251–1255. doi:10.1038/nbt1346. PMC 2814061Freely accessible. PMID 17989687.  open access publication – free to read
  66. ^ "OntoSelect". Retrieved 10 February 2011. 
  67. ^ "Ontaria". Retrieved 10 February 2011. 
  68. ^ Oberle, Daniel (2014). "How ontologies benefit enterprise applications" (PDF). Semantic Web Journal. IOS Press. 5 (6): 473–491. doi:10.3233/SW-130114. 
  69. ^ Frank, Andrew U. (2001). "Tiers of ontology and consistency constraints in geographical information systems". International Journal of Geographical Information Science. 15 (7): 667–678. doi:10.1080/13658810110061144. 

Further reading

External links