Automatic domain-specific learning: towards a methodology for ontology enrichment

Autores/as

  • Pedro Ureña Gómez-Moreno Universidad de Las Palmas de Gran Canaria
  • Eva M. Mestre-Mestre Universitat Politècnica de València

Palabras clave:

Ontology learning, FunGramKB, Corpus, Terminology, Biology

Resumen

At the current rate of technological development, in a world where enormous amount of data are constantly created and in which the Internet is used as the primary means for information exchange, there exists a need for tools that help processing, analyzing and using that information. However, while the growth of information poses many opportunities for social and scientific advance, it has also highlighted the difficulties of extracting meaningful patterns from massive data. Ontologies have been claimed to play a major role in the processing of large-scale data, as they serve as universal models of knowledge representation, and are being studied as possible solutions to this. This paper presents a method for the automatic expansion of ontologies based on corpus and terminological data exploitation. The proposed “ontology enrichment method” (OEM) consists of a sequence of tasks aimed at classifying an input keyword automatically under its corresponding node within a target ontology. Results prove that the method can be successfully applied for the automatic classification of specialized units into a reference ontology.

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Biografía del autor/a

Pedro Ureña Gómez-Moreno, Universidad de Las Palmas de Gran Canaria

Pedro Ureña Gómez-Moreno is Assistant professor at the Department of Didactics of Language and Literature at the University of Granada (Spain), where he develops most of his teaching and research activity. His teaching focuses on Natural Language Processing, Corpus Linguistics and English as a Second Language, both at the University of Granada and the UNED. His main areas of research are Morphosyntax and Lexicology within the frameworks of Corpus Linguistics and Natural Language Processing, with a special interest in Terminology and Knowledge Engineering applied to the development of FunGramKB Knowledge Base. A second line of research concerns the application of new technologies to language teaching and the development of virtual courses. He has authored and co-authored a number of refereed book chapters in Mouton de Gruyter and John Benjamins, as well as several articles in national and international journals, including The International Journal of Corpus Linguistics, Onomázein or The LSP Journal.

Eva M. Mestre-Mestre, Universitat Politècnica de València

Eva M. Mestre-Mestre works as associate professor at Universitat Politècnica de València. Since her Ph.D. thesis on the pragmatic implications of errors in English as a second language, her research has focused on Pragmatics, English learning in higher education, and corpus management, including computational linguistics, resulting in publications indexed in nationally and internationally prestigious journals, such as RESLA, or the Yearbook of Pragmatics. Apart from several book chapters, she has co-edited Understanding Meaning and Knowledge Representation for Cambridge Scholars Press. She was a visitor researcher in several European and American universities. She is currently the director of the panel on pragmatics in the Spanish Society for Applied Linguistics, and director of the panel on ESP in the Spanish Society for Corpus Linguistics.

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Publicado

2017-12-05

Cómo citar

Gómez-Moreno, P. U., & Mestre-Mestre, E. M. (2017). Automatic domain-specific learning: towards a methodology for ontology enrichment. Revista De Lenguas Para Fines Específicos, 23(2), 63–85. Recuperado a partir de https://ojsspdc.ulpgc.es/ojs/index.php/LFE/article/view/919

Número

Vol. 23 Núm. 2 (2017): Revista de lenguas para fines específicos

Sección

Sección Monográfica/Special Issue

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