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Computational Intelligence and Neuroscience
Volume 2014, Article ID 438291, 12 pages
Research Article

Gradient Learning Algorithms for Ontology Computing

1School of Information and Technology, Yunnan Normal University, Kunming 650500, China
2School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China

Received 7 June 2014; Revised 18 September 2014; Accepted 6 October 2014; Published 29 October 2014

Academic Editor: Karim G. Oweiss

Copyright © 2014 Wei Gao and Linli Zhu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting.