Authors: Stoutenburg, Suzette K.¹; Kalita, Jugal¹; Ewing, Kaily²; Hines, Lisa M.³

Source: International Journal of Bioinformatics Research and Applications, Volume 6, Number 4, 11 October 2010 , pp. 384-401(18)

Publisher: Inderscience Publishers

Abstract:

In recent years, the number of shared biomedical ontologies has increased dramatically, resulting in a need for integration of these knowledge sources. Automated solutions to aligning ontologies address this growing need. However, only very recently, solutions for scalability of ontology alignment have begun to emerge. This research investigates scalability in alignment of large-scale ontologies. We present an alignment algorithm that bounds processing by selecting optimal subtrees to align and show that this improves efficiency without significant reduction in precision. We apply the algorithm in conjunction with our approach that includes modelling ontology alignment in a Support Vector Machine.

Keywords: HEALTHCARE AND BIOSCIENCES; Biosciences and Bioinformatics; COMPUTING AND MATHEMATICS; Computing Science, Applications and Software; HEALTHCARE AND BIOSCIENCES; Healthcare and Medical Engineering

Document Type: Research article

DOI: http://dx.doi.org/10.1504/IJBRA.2010.036001

Affiliations: 1: The College of Engineering and Applied Science, University of Colorado, Colorado Springs, CO 80918, USA. 2: The College of Letters, Arts and Sciences, University of Colorado, Colorado Springs, CO 80918, USA. 3: The College of Letters, Arts and Sciences, University of Colorado, Colorado Springs, CO 80918, USA

Publication date: 2010-10-11

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