Ontology learning problem has raised much attention in semantic structure expression and information retrieval. As a powerful tool, ontology is evenly employed in various subjects, such as neuroscience, medical science, pharmacopedia, chemistry, education and other social science. Ontology similarity measuring plays a vital role in practical implementations since essential issues of ontology mapping are also similarity calculating. In ontology function learning, one learns a real valued score function that assigns scores to each ontology vertex which corresponds to a concept. Thus, the similarity between vertices is determined by means of the absolute value of difference between their corresponding scores. In this paper, we report the new optimization algorithms for obtaining ontology function in view of ontology sparse vector learning. The implementation of ontology algorithms is mainly based on iterative calculation in which we consider the whole matrix version of framework and ontology sparse vector are updated in each iterative. The data results obtained from four simulation experiments reveal that our newly proposed ontology approach has high efficiency and accuracy in biology and plant science with regard to ontology similarity measure, and humanoid robotics and education science with regard to ontology mapping. [ABSTRACT FROM AUTHOR]