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When Language Survived, Music Resurrected and Computer Died: To the Problem of Covert Ontologies in Language

  • Anastasia KolmogorovaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 943)

Abstract

Creating formal ontologies is one of the current information science trends. However, in the context of taxonomies existing in natural languages another type of class hierarchy seems to be more important – the so-called “covert ontologies” that categorize entities in terms of crypto classes or hidden classes.

The research aims to examine localization of the three entities in the Russian language natural ontology, which seem very different from the formal point of view. These entities are: “language” (i.e. belongs to the formal class of systems), “music” (i.e. represents the formal class of perception or activity), and, finally, “computer” (i.e. embodies the formal class of equipment).

According to our preliminary observations, all three entities under discussion are conceptualized in Russian language as living systems. Our further analysis of 500 occurrences in which the three entities’ names adjoin verbs designing different steps of vitality cycle showed that “music” enters the class of mythic heroes or Demiurges, “language” belongs to the covert class of Humans; at last, “computer” integrates the class of pets.

The revealed properties of natural categorization due to the effects of covert ontology also influence the eventual semantic roles of exploring entities’ names.

Keywords

Covert ontologies Formal and natural semantics Crypto classes 

References

  1. 1.
    Aiken, D.W.: Essence and existence, transcendentalism and phenomenalism: aristotle’s answers to the questions of ontology. Rev. Metaphys. 45(1), 29–55 (1991)Google Scholar
  2. 2.
    Øhrstrøm, P., Andersen, J., Schärfe, H.: What has happened to ontology? In: Dau, F., Mugnier, M.-L., Stumme, G. (eds.) Conceptual Structures 2005, ICCS, pp. 135–147. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Husserl, E.: Formal and transcendental logic. Matinus Nijhoff, Nederlands (1969)CrossRefGoogle Scholar
  4. 4.
    Smith, B.: Logic and formal ontology. In: Mohanty, J.N., McKenna, W. (eds.) Husserl’s Phenomenology: A Textbook, Lanham, pp. 29–67. University Press of America, Lanham (1989)Google Scholar
  5. 5.
    Hartmann, N.: New Ways of Ontology. Henry Regnery Company, Chicago (1953)Google Scholar
  6. 6.
    Hovy, E., Knight, K., Junk, M.: Large Resources. Ontologies (SENSUS) and Lexicons. http://www.isi.edu/natural-language/projects/ONTOLOGIES.html. Accessed 24 May 2017
  7. 7.
    Gruber, Th.: What is an Ontology. http://www-ksl.stanford.edu/kst/what-is-an-ontology.html. Accessed 31 May 2017
  8. 8.
    Guarino, N.: Understanding, Building, and Using Ontologies. http://ksi.cpsc.ucalgary.ca/KAW/KAW96/guarino/guarino.html. Accessed 05 Jun 2017
  9. 9.
  10. 10.
    Humboldt, W.v.: On language. On the Diversity of Human Language Construction and its Influence on the Mental Development of the Human Species. Cambridge University Press, Cambridge (1999)Google Scholar
  11. 11.
    Whorf, B.L.: Language: Thought and Reality. Cambridge University Press, Cambridge (1956)Google Scholar
  12. 12.
    Vinogradov, V.A.: Nominative categories in songai. In: Vinogradov, V.A. (ed.) Foundations of African linguistics. Nominative categories, vol. 1. Aspect Press, Moscow (1997)Google Scholar
  13. 13.
    Kretov, A.A., Titov, V.T.: The role of covert categories in typological description of Romance languages. Bull. Voronej State Univ. Linguist. Cross-Cult. Commun. 1(6), 7–12 (2010)Google Scholar
  14. 14.
    Boriskina, O.O.: Crypto Classes in English. Publishing House Istoky, Voronej (2011)Google Scholar
  15. 15.
    Stefanowitsch, A.: Words and theirs metaphors: A corpus-based approach. In: Stefanowitsch, A., Gries, Th. (eds.) Corpus-based Approaches to Metaphor and Metonymy, pp. 63–105. Mouton de Gruyter, Berlin (2006)CrossRefGoogle Scholar
  16. 16.
    Church, K., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 1(16), 22–29 (1996)Google Scholar
  17. 17.
    Zakharov, V., Khokhlova, M.: Study of effectiveness of statistical measures for collocation extraction on Russian texts. Comput. Linguist. Intellect. Technol. 9(16), 137–143 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Siberian Federal UniversityKrasnoyarskRussia

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