Ontology (information science)

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.[1]

Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.[2] For instance, the definition and ontology of economics is a primary concern in Marxist economics,[3] but also in other subfields of economics.[4] An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management).

What ontologies in both information science and philosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in information science),[5] and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence).

Applied ontology is considered by some as a successor to prior work in philosophy. However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles, or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained considerable attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields, including biomedical informatics,[6] industry.[7] Such efforts often use ontology editing tools such as Protégé.[8]

  1. ^ Ontology. McGill–Queen's University Press. 2002-11-26. p. 4. ISBN 9780773582675. Applied ontology, as discipline or domain, is scientific in that it applies the definition of being to determine the ontological commitments of other disciplines, notably but not exclusively in the natural sciences, in much the same way that applied mathematics in engineering is related to pure mathematics.
  2. ^ G Budin (2005), "Ontology-driven translation management", in Helle V. Dam (ed.), Knowledge Systems and Translation, Jan Engberg, Heidrun Gerzymisch-Arbogast, Walter de Gruyter, p. 113, ISBN 978-3-11-018297-2
  3. ^ 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 – via Oxford Journals.
  4. ^ Zuniga, Gloria L. (1999-02-02). "An Ontology Of Economic Objects". Mpra Paper. Research Division of the Federal Reserve Bank of St. Louis. Retrieved 2013-06-16.
  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. ^ {Bioportal
  7. ^ Industrial Ontologies Foundry
  8. ^ Musen, Mark (2015). "The Protégé Project: A Look Back and a Look Forward". AI Matters. 1 (4): 4–12. doi:10.1145/2757001.2757003. PMC 4883684. PMID 27239556.

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