BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts

  • Authors:

  • Juana MaríA Ruiz-MartíNez;Rafael Valencia-GarcíA;Rodrigo MartíNez-BéJar;Achim Hoffmann

  • Affiliations:

  • Computer Science Faculty, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain;Computer Science Faculty, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain;Computer Science Faculty, University of Murcia, Campus de Espinardo, 30100 Murcia, Spain;School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

The Semantic Web can be conceived as an extension of the current Web where information is given well-defined meaning. In this scenario ontologies are crucial since they provide meaning and facilitate the search for contents and information. Ontology population is a knowledge acquisition activity used to transform data sources into instance data. The instantiation of ontologies with new knowledge is an important step towards the provision of valuable ontology-based services. In this paper, we present a methodology to be used for ontology population. For it, top level ontologies that define the basic semantic relations in biomedical domains are mapped onto semantic role labelling resources, where every semantic role defines the role of a verbal argument in the event expressed by the verb. The modular architecture employed in our work gives the system a high versatility, as resources have been developed separately and they can be easily adapted to most biomedical domain ontologies.