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In order to represent the semantics and concepts better, the ontology, as an efficient model, has penetrated into all research areas of the computer science and information technology. In recent years, the ontology framework has also been applied to biology, chemistry, pharmaceutics, geography, and other fields, and it has attracted much attention from the researchers. Multi-dividing ontology algorithm is one of the popular learning approaches for ontology applications in which the vertex set and sample data are divided into several parts. In this paper, we introduce a new ontology optimization scheming by means of representer theorem and kernel function, and the method is a kind of linear programming. Four experiments are designed for the application of ontology algorithms in different fields to test the effectiveness by comparing the implement data. [ABSTRACT FROM AUTHOR]

Ontology, as a useful tool, is widely applied in lots of fields such as geography science, computer science and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A popular trick is to make use of the similarity between vertices on ontology graphs. It relies on an ontology function that maps the vertex set of an ontology graph to real numbers, and multi-dividing is an effective approach to achieve this goal. In this paper, we report the generalization bounds and uniform bounds for kernel-based multi-dividing ontology algorithms, which are stated as regularization schemes. The ontology loss function is convex and meets Lipschitz assumption. The results are obtained in terms of statistical probability inequality and empirical Rademacher complexity. [ABSTRACT FROM AUTHOR]

Along with the arrival of the era of large amount of data, many machine learning methods have been applied to the ontology similarity calculation and ontology mapping. In this paper, we raise an infinite push model for ontology similarity measuring and ontology mapping in multi-dividing setting. The iterative algorithm and generalization bound are given by virtue of the dual solution for optimization and the trick of covering number approach. Furthermore, the fast ontology algorithm for standard ontology SVM by virtue of infinite push multi-dividing ontology algorithm is obtained. Finally, four experiments presented on various fields verify the efficiency of the new computation model for ontology similarity measuring and ontology mapping applications in multi-dividing setting. [ABSTRACT FROM AUTHOR]

Ontology is an effective tool for processing concept semantics, and in the ontology learning algorithm, all the semantic information of each vertex is expressed by a multidimensional vector. The essence of ontology learning algorithm is to obtain ontology function in terms of ontology data samples, so as to map each concept in ontology to a real number. Stability is the foundation of the ontology learning algorithm and the guarantee of its generalization ability. This article relaxes the original uniformly stable hypothesis and proposes the concept of locally ontology relaxed stability. And under the setting of reproducing kernel Hilbert space, the upper bound of stability is verified. Under the framework of random ontology algorithm, the original concept is redefined. The error bounds in general, the reproducing kernel Hilbert space and the stochastic ontology learning algorithm frameworks are obtained in terms of their respective stability definitions. [ABSTRACT FROM AUTHOR]

As a semantic representation model, ontology has penetrated into all areas of natural science and social science. The core issue of ontology applications is similarity computing between ontology concepts. In this article, we report a sparse vector learning algorithm for ontology similarity measure and ontology mapping in terms of sub-gradient calculating and iterative computation. The main procedure of our iterative computation is based on the tricks of fuse and divide. The simulation experimental results show that the new proposed algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in biology and physical education science. [ABSTRACT FROM AUTHOR]

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]

Ontology, as an effective structured information storage, classification, and statistical tool, has been attached importance from researchers of various disciplines. At present, ontology has become the core study field of semantic network, artificial intelligence, and information retrieval. The core topic of ontology algorithm is to calculate the similarity between concepts, so as to obtain the potential information of each other. In this paper, we present the boosting based iterative learning algorithm to compute the ontology sparse vector, and thus get the ontology function which maps each ontology concept to a real number. Since the advantage of ontology sparse vector is dimensionality reduction, the algorithm can extract the key information from the high dimensional ontology representation data. At last, we select four classical ontologies to test the efficiency of new ontology algorithm, and the rest give the positive answer. [ABSTRACT FROM AUTHOR]

In recent years, the ontology problem has gained attention in machine learning and it has many applications in various fields. Ontology similarity computation plays a critical role in practical implementations. In ontology learning setting, one learns a real-valued score function that assigns scores to ontology vertices. Then, the similarity between vertices is weighted in terms of the difference between their corresponding scores. The purpose of this paper is to report a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of magnitude preserving. The classes of ontology loss function are considered in regularization ontology framework, the ontology function is supposed to be linear, and the gradient descent implement is presented for getting the optimal ontology function. The result data from our four simulation experiments imply that the new proposed ontology trick 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]