International Journal of Automation, Control and Intelligent Systems
Articles Information
International Journal of Automation, Control and Intelligent Systems, Vol.1, No.1, May 2015, Pub. Date: May 13, 2015
Ontology Similarity Measuring and Ontology Mapping Algorithms Based on Graph-Constrained Elastic-Net
Pages: 9-15 Views: 1433 Downloads: 678
Authors
[01] Yun Gao, Department of Editorial, Yunnan Normal University, Kunming, China.
[02] Li Liang, School of Information Science and Technology, Yunnan Normal University, Kunming China.
[03] Wei Gao, School of Information Science and Technology, Yunnan Normal University, Kunming China.
Abstract
Ontology similarity calculation is important research topics in information retrieval and widely used in science and engineering. By analyzing the technology of GraphNet, we propose the new algorithm for ontology similarity measure and ontology mapping. Via the ontology sparse vector learning, the ontology graph is mapped into a line consists of real numbers. The similarity between two concepts then can be measured by comparing the difference between their corresponding real numbers. The experiment results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping.
Keywords
Ontology, Similarity Measure, Ontology Mapping, Sparse Vector, Graph-Constrained Elastic-Net
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