Yun Gao
Department of Editorial Yunnan Normal University, 650092, Kunming, China
Li Liang
School of Information Science and Technology, Yunnan Normal University, 650500, Kunming, China
Wei Gao
School of Information Science and Technology, Yunnan Normal University, 650500, Kunming, China
ABSTRACT
The aim of dimensionality reduction is to use low-dimensional data to represent high-dimensional data and thus can reduce the computational complexity. Graph spectral is an effective dimension reduction technology and used in various fields of computer science. In this study, we propose new algorithm for ontology similarity and ontology mapping application. The algorithm is given by calculating the similarity matrix in terms of spectral dimensionality reduction. Two experiments are designed to manifest the effectiveness of the algorithm.
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How to cite this article
Yun Gao, Li Liang and Wei Gao, 2013. Similarity Matrix Learning Using Dimensionality Reduction for Ontology Applications. Information Technology Journal, 12: 7442-7447.
DOI: 10.3923/itj.2013.7442.7447
URL: https://scialert.net/abstract/?doi=itj.2013.7442.7447
DOI: 10.3923/itj.2013.7442.7447
URL: https://scialert.net/abstract/?doi=itj.2013.7442.7447
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