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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 or fundamentally exist for a particular "domain of discourse. It is thus a practical application of philosophical "ontology, with a "taxonomy.

An ontology 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 to organize information. The ontology can then be applied to "problem solving.


Etymology and definition[edit]

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).[3]


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),[4] and debates concerning whether a "normative ontology is viable (e.g., debates over "foundationalism in philosophy, and over 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[5] where it is a primary concern, but also in other subfields.[6] Such concerns intersect with those of information science when a simulation or model is intended to enable decisions in the economic realm; for example, to determine what "capital assets are at risk and if so by how much (see "risk management). Some claim 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.[7]

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. 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.

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.[8]

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[9] 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.[10]

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.[11] 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."[12]


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[edit]

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,[13] but this area of research is still largely theoretical.

Upper ontology[edit]

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.

There are several standardized upper ontologies available for use, including "BFO, "BORO method, "Dublin Core, "GFO, "OpenCyc/"ResearchCyc, "SUMO, "UMBEL, the Unified Foundational Ontology (UFO),[14] and "DOLCE.[15][16] "WordNet, while considered an upper ontology by some, is not strictly an ontology. However, it has been employed as a linguistic tool for learning domain ontologies.[17]

Hybrid ontology[edit]

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.[18] An evaluation of two most established ontology visualization techniques: indented tree and graph is discussed in.[19] A visual language for ontologies represented in "OWL is specified by the Visual Notation for OWL Ontologies (VOWL).[20]


"Ontology engineering (or ontology building) is a subfield of "knowledge engineering. It studies the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them.[21][22]

Ontology engineering aims to make explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, such as the obstacles related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies for a particular domain.[23]

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.


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 methods have been explored to automatically link ontologies to documents, e.g. in the context of the BioCreative challenges.[24]


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

Published examples[edit]

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.

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

Examples of applications[edit]

In general, ontologies can be used beneficially in

See also[edit]

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. ^ Garshol, L. M. (2004). "Metadata? Thesauri? Taxonomies? Topic Maps! Making sense of it all". Retrieved 13 October 2008. 
  4. ^ 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. 
  5. ^ 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. 
  6. ^ 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. 
  7. ^ 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. 
  8. ^ "Gruber, T. (2008). Liu, Ling; Özsu, M. Tamer, eds. Ontology. Encyclopedia of Database Systems. Springer-Verlag. "ISBN "978-0-387-49616-0. 
  9. ^ "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. 
  10. ^ "Gruber, T. (2001). "What is an Ontology?". "Stanford University. Retrieved 2009-11-09. 
  11. ^ "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 
  12. ^ 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. 
  13. ^ "Project: Dynamic Ontology Repair". University of Edinburgh Department of Informatics. Retrieved 2 January 2012. 
  14. ^ Giancarlo Guizzardi & Gerd Wagner. "A Unified Foundational Ontology and some Applications of it in Business Modeling" (PDF). Retrieved 31 March 2014. 
  15. ^ a b "Laboratory for Applied Ontology - DOLCE". Laboratory for Applied Ontology (LOA). Retrieved 10 February 2011. 
  16. ^ a b "OWL version of DOLCE+DnS". Semantic Technology Lab. Retrieved 21 February 2013. 
  17. ^ 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. 
  18. ^ 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. 
  19. ^ 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. 
  20. ^ Negru, Stefan; Lohmann, Steffen; Haag, Florian (7 April 2014). "VOWL: Visual Notation for OWL Ontologies: Specification of Version 2.0". Visual Data Web. 
  21. ^ 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. 
  22. ^ 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/ 
  23. ^ Pouchard, Line; Ivezic, Nenad; Schlenoff, Craig (March 2000). "Ontology Engineering for Distributed Collaboration in Manufacturing" (PDF). Proceedings of the AIS2000 conference. 
  24. ^ 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. 
  25. ^ "SADL". "Sourceforge. Retrieved 10 February 2011. 
  26. ^ "AURUM - Information Security Ontology". Retrieved 29 January 2016. 
  27. ^ "Basic Formal Ontology (BFO)". Institute for Formal Ontology and Medical Information Science (IFOMIS). 
  28. ^ "BioPAX". Retrieved 10 February 2011. 
  29. ^ Osterwalder, Alexander; "Pigneur, Yves (June 17–19, 2002). "An e-Business Model Ontology for Modeling e-Business" (PDF). "15th Bled eConference, "Slovenia. 
  30. ^ 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. 
  31. ^ "About CCO and GexKB". Semantic Systems Biology. 
  32. ^ "CContology". Retrieved 10 February 2011. 
  33. ^ "The CIDOC Conceptual Reference Model (CRM)". Retrieved 10 February 2011. 
  34. ^ "COSMO". MICRA Inc. Retrieved 10 February 2011. 
  35. ^ 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. 
  36. ^ "Financial Industry Business Ontology (FIBO)". Retrieved 15 March 2017. 
  37. ^ "Foundational, Core and Linguistic Ontologies". Retrieved 10 February 2011. 
  38. ^ "Foundational Model of Anatomy". Retrieved 10 February 2011. 
  39. ^ "GOLD". Retrieved 10 February 2011. 
  40. ^ "Generalized Upper Model". Retrieved 10 February 2011. 
  41. ^ "The IDEAS Group Website". Retrieved 10 February 2011. 
  42. ^ "Linkbase". Retrieved 10 February 2011. 
  43. ^ "Bioportal". National Center for Biological Ontology (NCBO). 
  44. ^ "Ontology browser for most of the Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP). 
  45. ^ "The Open Biological and Biomedical Ontologies". Berkeley Bioinformatics Open Source Project (BBOP). 
  46. ^ "OMNIBUS Ontology". Retrieved 10 February 2011. 
  47. ^ "ONSTR". Retrieved 16 April 2014. 
  48. ^ "Plant Ontology". Retrieved 10 February 2011. 
  49. ^ "PRO". Retrieved 10 February 2011. 
  50. ^ "Protein Ontology". Retrieved 10 February 2011. 
  51. ^ 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. 
  52. ^ "SWEET". Retrieved 10 February 2011. 
  53. ^ 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. 
  54. ^ "YAMATO". Retrieved 10 February 2011. 
  55. ^ "COLORE". Retrieved 4 May 2011. 
  56. ^ "DAML Ontology Library". Retrieved 10 February 2011. 
  57. ^ "ODP Library". Retrieved 21 February 2013. 
  58. ^ "Protege Ontology Library". Retrieved 10 February 2011. 
  59. ^ "SchemaWeb". Retrieved 10 February 2011. 
  60. ^ "OBO Foundry". Retrieved 10 February 2011. 
  61. ^ "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
  62. ^ "OntoSelect". Retrieved 10 February 2011. 
  63. ^ "Ontaria". Retrieved 10 February 2011. 
  64. ^ Oberle, Daniel (2014). "How ontologies benefit enterprise applications" (PDF). Semantic Web Journal. IOS Press. 5 (6): 473–491. "doi:10.3233/SW-130114. 
  65. ^ 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[edit]

External links[edit]

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