OntoEnrich: Una plataforma para el análisis léxico de ontologías orientado a su enriquecimiento axiomático

  1. Manuel Quesada Martínez
  2. Dagoberto Castellanos Nieves
  3. Jesualdo T. Fernández Breis
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2017

Issue: 59

Pages: 171-174

Type: Article

More publications in: Procesamiento del lenguaje natural

Abstract

We present OntoEnrich, an online platform for the automatic detection and guided analysis of lexical regularities in ontology labels. An analysis guided by these regularities permits users to explore different lexical and semantic aspects as the application of the OBO Foundry principles in biomedical ontologies. The goal of this demonstration is to show some use cases obtained after applying OntoEnrich in two relevant biomedical ontologies such as Gene Ontology and SNOMED CT. Thus, we will show how the performed analysis could be used to elucidate hidden semantics from the natural language fragments (human-friendly), and how this could be used to enrich the ontology by generating new logical axioms (machine-friendly).

Funding information

Este trabajo ha sido posible gracias al Mi-nisterio de Economía y Competitividad y el Fondo Europeo de Desarrollo Regional (FE-DER), a través del proyecto TIN2014-53749-C2-2-R, y a la Fundación Séneca a través del proyecto 19371/PI/14.

Funders

Bibliographic References

  • Aguilar, C. A., O. Acosta, G. Sierra, S. Juárez y T. Infante. 2016. Extracción de contextos definitorios en el área de biomedicina. Procesamiento del Lenguaje Natural, 57:167–170.
  • Brewster, C., S. Jupp, J. Luciano, D. Shotton, R. D. Stevens y Z. Zhang. 2009. Issues in learning an ontology from text. BMC bioinformatics, 10(5):S1.
  • Buitelaar, P., P. Cimiano y B. Magnini. 2005. Ontology learning from text: methods, evaluation and applications, volumen 123. IOS press.
  • Fernandez-Breis, J., L. Iannone, I. Palmisano, A. Rector y R. Stevens. 2010. Enriching the Gene Ontology via the dissection of labels using the ontology pre-processor language. Know. Engineering and Management by Masses,páginas 59–73. Springer.
  • Golbreich, C., J. Grosjean y S. J. Darmoni. 2013. The Foundational Model of Anatomy in OWL 2 and its use. Artificial Intelligence in Medicine, 57(2):119–132.
  • Guarino, N. 1998. Formal Ontology in Information Systems: Proceedings of the 1st International Conference June 6-8, 1998, Trento, Italy, páginas 3-15. IOS Press.
  • Mungall, C. J., M. Bada, T. Z. Berardini, J. Deegan, A. Ireland, M. A. Harris, D. P. Hill y J. Lomax. 2011. Cross-product extensions of the Gene Ontology. Journal of Biomedical Informatics, 44(1):80–86.
  • Quesada-Martínez M. 2015. Methodology for the enrichment of biomedical knowledge resources. Ph.D. tesis, Depto. de Informática y Sistemas. Univ. de Murcia.
  • Rector, A. y L. Iannone. 2012. Lexically suggest, logically define: Quality assurance of the use of qualifiers and expected results of post-coordination in SNOMED. Journal of Biomedical Informatics, 45:199–209.
  • Smith, B., M. Ashburner, C. Rosse, J. Bard, W. Bug y others. 2007. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature biotechnology, 25(11):1251-1255.
  • Third, A. 2012. “Hidden Semantics”: What Can We Learn from the Names in an Ontology? En Proceedings of the 7th International Natural Language Generation Conference INLG '12 páginas 67–75. ACL.