Search

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]

Ontology, as a useful tool, has been widely applied in various fields, and ontology concept similarity calculation is an essential problem in these application algorithms. A recent method to get similarity between vertices on ontology is not by pairwise computation but based on a function which maps ontology graph into a line and maps every vertex in graph into a real-value, the similarity is measured by the difference of their corresponding scores. The multi-dividing method is suitable for ontology problem and plays a key role in achieving this. Such ontology function is given by learning a training sample which contains a subset of vertices with k classes from ontology graph. In this paper, we propose a new multi-dividing ontology algorithm framework, which is designed to avoid the choice of loss function. Meanwhile, there is such a vertex selection policy in new multi-dividing ontology algorithm that it guarantees that the new algorithm can be employed for an ontology graph with its structure rather than a tree. We provide some theoretical characteristics of the new multi-dividing ontology algorithm, and show that the new algorithm is convergent. [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]

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]

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]