Title: Learning rate of gradient descent multi-dividing ontology algorithm

Authors: Jianzhang Wu; Xiao Yu; Wei Gao

Addresses: School of Computer Science and Engineer, Southeast University, Nanjing 210096, China ' School of Continuing Education, Southeast University, Nanjing 210096, China ' School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China

Abstract: As acknowledge representation model, ontology has wide applications in information retrieval and other disciplines. Ontology concept similarity calculation is a key issue in these applications. One approach for ontology application is to learn an optimal ontology score function which maps each vertex in graph into a real-value. And the similarity between vertices is measured by the difference of their corresponding scores. The multi-dividing ontology algorithm is an ontology learning trick such that the model divides ontology vertices into k parts correspond to the k classes of rates. In this paper, we propose the gradient descent multi-dividing ontology algorithm based on iterative gradient computation and yield the learning rates with general convex losses by virtue of the suitable step size and regularisation parameter selection.

Keywords: similarity measures; ontology mapping; stochastic gradient descent; multi-dividing setting; learning rate; ontology concept similarity; ontology vertices.

DOI: 10.1504/IJMTM.2014.066699

International Journal of Manufacturing Technology and Management, 2014 Vol.28 No.4/5/6, pp.217 - 230

Available online: 01 Jan 2015 *

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