Supporting the discovery and labeling of non-taxonomic relationships in ontology learning

Authors:
J. Villaverde;A. Persson;D. Godoy;A. Amandi
Affiliations:
ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco, CP 7000, Tandil, Bs. As., Argentina and CONICET, Consejo Nacional de Investigaciones Científicas y T&# ...;ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco, CP 7000, Tandil, Bs. As., Argentina and CONICET, Consejo Nacional de Investigaciones Científicas y T&# ...;ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco, CP 7000, Tandil, Bs. As., Argentina and CONICET, Consejo Nacional de Investigaciones Científicas y T&# ...;ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco, CP 7000, Tandil, Bs. As., Argentina and CONICET, Consejo Nacional de Investigaciones Científicas y T&# ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Ontology learning (OL) from texts has been suggested as a technology that helps to reduce the bottleneck of knowledge acquisition in the construction of domain ontologies. In this learning process, the discovery, and possibly also labeling, of non-taxonomic relationships has been identified as one of the most difficult and often neglected problems. In this paper, we propose a technique that addresses this issue by analyzing a domain text corpus to extract verbs frequently applied for linking certain pairs of concepts. Integrated in an ontology building process, this technique aims to reduce the work-load of knowledge engineers and domain experts by suggesting candidate relationships that might become part of the ontology as well as prospective labels for them.