scholarly journals SVM-Based Multi-Dividing Ontology Learning Algorithm and Similarity Measuring on Topological Indices

2020 ◽  
Vol 8 ◽  
Author(s):  
Linli Zhu ◽  
Gang Hua ◽  
Haci Mehmet Baskonus ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Topological Indices ◽  
Ontology Learning ◽  
Similarity Measuring

Partial multi-dividing ontology learning algorithm

2018 ◽  
Vol 467 ◽  
pp. 35-58 ◽  
Author(s):  
Wei Gao ◽  
Juan L.G. Guirao ◽  
B. Basavanagoud ◽  
Jianzhang Wu
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

2015 ◽  
Vol 25 (14) ◽  
pp. 1540034 ◽  
Author(s):  
Wei Gao ◽  
Linli Zhu ◽  
Kaiyun Wang
Keyword(s):  
Physics Education ◽  
Learning Algorithm ◽  
Augmented Lagrangian Method ◽  
Ontology Mapping ◽  
Sparse Learning ◽  
Alternating Direction ◽  
Learning Techniques ◽  
Sparse Vector ◽  
Plant Ontology ◽  
Similarity Measuring

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Disequilibrium multi-dividing ontology learning algorithm

2017 ◽  
Vol 46 (18) ◽  
pp. 8925-8942 ◽  
Author(s):  
Jianzhang Wu ◽  
Xiao Yu ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning

Leave-two-out stability of ontology learning algorithm

2016 ◽  
Vol 89 ◽  
pp. 322-327 ◽  
Author(s):  
Jianzhang Wu ◽  
Xiao Yu ◽  
Linli Zhu ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Ontology Learning

scholarly journals Graph Learning-Based Ontology Sparse Vector Computing

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1562
Author(s):  
Jianzhang Wu ◽  
Arun Kumar Sangaiah ◽  
Wei Gao
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Optimal Solution ◽  
Spectral Graph Theory ◽  
Optimization Techniques ◽  
Ontology Learning ◽  
Structure Information ◽  
Sparse Vector ◽  
Vector Computing ◽  
The Given

The ontology sparse vector learning algorithm is essentially a dimensionality reduction trick, i.e., the key components in the p-dimensional vector are taken out, and the remaining components are set to zero, so as to obtain the key information in a certain ontology application background. In the early stage of ontology data processing, the goal of the algorithm is to find the location of key components through the learning of some ontology sample points, if the relevant concepts and structure information of each ontology vertex with p-dimensional vectors are expressed. The ontology sparse vector itself contains a certain structure, such as the symmetry between components and the binding relationship between certain components, and the algorithm can also be used to dig out the correlation and decisive components between the components. In this paper, the graph structure is used to express these components and their interrelationships, and the optimal solution is obtained by using spectral graph theory and graph optimization techniques. The essence of the proposed ontology learning algorithm is to find the decisive vertices in the graph Gβ. Finally, two experiments show that the given ontology learning algorithm is effective in similarity calculation and ontology mapping in some specific engineering fields.

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