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Ontology, a common tool in various fields of natural sciences, aims to get the optimal ontology similarity calculation function. Favored by researchers from semantic query and other disciplines, it is used to calculate the similarity between ontology concepts. The semantic information of each concept is expressed by a d-dimensional vector, and the similarity calculation is transformed into the geometric distance calculation of the two corresponding vectors. Using the Mahalanobis distance calculation formula, the ontology algorithm can contribute to getting the optimal distance matrix. In this paper, in terms of the coordinate descent trick and iterative method, we get the ontology distance matrix learning algorithm, and then apply it to ontology similarity computation and ontology mapping. Moreover, the ontology distance matrix learning approach in the manifold setting is discussed, and its kernel solution is studied as well. The main ontology learning algorithm is illustrated by a comparison of other ontology algorithmic data in a specific application context. [ABSTRACT FROM AUTHOR]

In recent years, many learning technologies have been applied in ontology similarity measuring and ontology mapping via learning an ontology function f : V → R which maps an ontology graph to the real line. In these settings, all the information for an ontology vertex (corresponding to concept) is expressed as a vector. However, in a special application, the value of ontology function for each ontology vertex is determined by a few components of the vector. The aim of feature extraction for ontology vector is to obtain these components to fix the index set of the vector, and such a procedure is equivalent to learning an ontology sparse vector in which most components are zero. In this paper, we raise an ontology sparse vector learning model for ontology similarity measuring and ontology mapping in terms of SOCP. The balance term consists of Ω norm, and the directed acyclic graph is employed in ontology setting for backward and forward procedure. Then, the active index set algorithm is designed to moderate the value of p, thus applications will be extended. Finally, five experiments are presented on various fields to verify the efficiency of the new ontology algorithm for ontology similarity measuring and ontology mapping in multidisciplinary research. [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]

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]