Affiliations: [a] Department of Computer Science, Guangdong University Science and Technology, Dongguan, China
| [b] Department of Mathematical Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| [c] School of Information Science and Technology, Yunnan Normal University, Kunming, China
Correspondence:
[*]
Corresponding author. Shu Gong, Department of Computer Science, Guangdong University Science and Technology, Dongguan 523083, China. E-mail: [email protected].
Abstract: From the mathematical point of view, the goal of ontology learning is to obtain the dimensionality function
f:ℝp→ℝ
, and the p-dimensional vector corresponding to the ontology vertex is mapped into one-dimensional real number. In the background of big data applications, the ontology concept corresponds to the high complexity of information, and thus sparse tricks are used in ontology learning algorithm. Through the ontology sparse vector learning, the ontology function f is obtained via ontology sparse vector β, and then applied to ontology similarity computation and ontology mapping. In this paper, the ontology optimization strategy is obtained by coordinate descent and dual optimization, and the optimal solution is obtained by iterative procedure. Furthermore, the greedy method and active sets are applied in the iterative process. Two experiments are presented where we will apply our algorithm to plant science for ontology similarity measuring and to mathematics ontologies for ontology mapping, respectively. The experimental data show that our primal dual based ontology sparse vector learning algorithm has high efficiency.
