The Open Cybernetics & Systemics Journal

2015, 9 : 657-662
Published online 2015 June 26. DOI: 10.2174/1874110X01509010657
Publisher ID: TOCSJ-9-657

Ontology Sparse Vector Learning Based on Accelerated First-Order Method

Yun Gao and Wei Gao
School of Information and Technology, Yunnan Normal University, Kunming 650500, China.

ABSTRACT

In this article, we present a sparse vector learning algorithm for ontology similarity measure and ontology mapping by virtue of accelerated first-order technology. The main procedure of our iterative algorithm is relying on proximity operator computation, Picard-Opial process and accelerated first-order tricks. The simulation experimental results show that the new proposed algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in plant science and university application.

Keywords:

Accelerated first-order method, ontology mapping, ontology, similarity measure, sparse vector.