title: Gradient learning algorithms for ontology computing.
abstract: 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.
authors.affiliation: School of Information and Technology, Yunnan Normal University, Kunming 650500, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Gao
authors.affiliation: School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China.
authors.forename: Linli
authors.initials: L
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D064229:Biological Ontologies; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D006801:Humans; D010944:Plants; D012371:Robotics