Building ontological meaning in a lexico-conceptual knowledge base

  1. Rocío Jiménez Briones 1
  2. Alba Luzondo Oyón 2
  1. 1 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Journal:
Onomázein: Revista de lingüística, filología y traducción de la Pontificia Universidad Católica de Chile

ISSN: 0717-1285

Year of publication: 2011

Issue: 23

Pages: 11-40

Type: Article

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More publications in: Onomázein: Revista de lingüística, filología y traducción de la Pontificia Universidad Católica de Chile

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Metrics

Cited by

  • Scopus Cited by: 11 (08-05-2023)
  • Dialnet Metrics Cited by: 8 (01-06-2023)
  • Web of Science Cited by: 11 (18-05-2023)

JCR (Journal Impact Factor)

  • Year 2011
  • Journal Impact Factor: 0.094
  • Journal Impact Factor without self cites: 0.031
  • Article influence score: 0.0
  • Best Quartile: Q4
  • Area: LINGUISTICS Quartile: Q4 Rank in area: 139/162 (Ranking edition: SSCI)

CIRC

  • Social Sciences: B
    •
  • Human Sciences: A
    •

Scopus CiteScore

  • Year 2011
  • CiteScore of the Journal : 0.0
  • Area: Language and Linguistics Percentile: 4
  • Area: Linguistics and Language Percentile: 3

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Abstract

Framed within the world of Artificial Intelligence, and more precisely within the project FunGramKB, i.e. a user-friendly environment for the semiautomatic construction of a multipurpose lexico-conceptual knowledge base for Natural Language Processing systems, the aim of this paper is two-fold. Firstly, we shall provide a necessarily non-exhaustive theoretical discussion of FunGramKB in which we will introduce the main elements that make up its Ontology (i.e. Thematic Frames, Meaning Postulates, different types of concepts, etc.). Secondly, we will describe the meticulous process carried out by knowledge engineers when populating this conceptually-driven Ontology. In doing so, we shall examine various examples belonging to the domain of �change� or #TRANSFORMATION (in the COREL notation), in an attempt to show how conceptual knowledge can be modeled in for Artificial Intelligence purposes.

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