Skip to main content



Computational ontology; Ontological engineering; Semantic data model


In thecontext of computer and information sciences, an ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals. Ontologies are typically specified in languages that allow abstraction away from data structures and implementation strategies; in practice, the languages of ontologies are closer...


  • Level Specification Semantics
  • Low-level Data Model
  • Conventional Database Models
  • Static Semantic Constraints
  • Cross-database Search

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Recommended Reading

  1. Berners-Lee T, Hendler J, Lassila O. The semantic web. Scientific American; 2001.

    Google Scholar 

  2. Gruber TR. A translation approach to portable ontology specifications. Knowl Acquis. 1993;5(2):199–220.

    CrossRef  Google Scholar 

  3. Gruber TR. Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud. 1995;43(5–6):907–28.

    CrossRef  Google Scholar 

  4. Guarino N. Formal ontology, conceptual analysis and knowledge representation. Int J Hum Comput Stud. 1995;43(5–6):625–40.

    CrossRef  Google Scholar 

  5. Hayes PJ. The second naive physics manifesto. In: Moore RC, Hobbs J, editors. Formal theories of the common-sense world. Norwood: Ablex; 1985.

    Google Scholar 

  6. McCarthy J. Circumscription – a form of non-monotonic reasoning. Artif Intell. 1980;5(13):27–39.

    CrossRef  MATH  Google Scholar 

  7. McGuinness DL, van Harmelen F. OWL web ontology language. W3C Recommendation, February 10, 2004. Available online at:

  8. Neches R, Fikes RE, Finin T, Gruber TR, Patil R, Senator T, Swartout WR. Enabling technology for knowledge sharing. AI Mag. 1991;12(3):16–36.

    Google Scholar 

  9. Smith B, Welty C. Ontology – towards a new synthesis. In: Proceedings of the international conference on formal ontology in information systems. 2001.

    Google Scholar 

  10. Sowa JF. Conceptual structures: information processing in mind and machine. Reading: Addison Wesley; 1984.

    MATH  Google Scholar 

  11. Standard Upper Ontology Working Group (SUO). IEEE P1600.1. Available online at:

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Tom Gruber .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer Science+Business Media LLC

About this entry

Cite this entry

Gruber, T. (2016). Ontology. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY.

Download citation

  • DOI:

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering