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Distance learning techniques for ontology similarity measuring and ontology mapping

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Abstract

Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

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Acknowledgements

We thank all the reviewers for their constructive suggestions on how to improve the quality of this paper.

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Authors and Affiliations

  1. School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China

    Wei Gao

  2. Department of Applied Mathematics, Iran University of Science and Technology, Narmak, Tehran, 16844, Iran

    Mohammad Reza Farahani

  3. Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore, Pakistan

    Adnan Aslam

  4. Department of Mathematics, Rani Channamma University, Belagavi, Karnataka, 591156, India

    Sunilkumar Hosamani

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  1. Wei Gao
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  2. Mohammad Reza Farahani
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  3. Adnan Aslam
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  4. Sunilkumar Hosamani
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Correspondence to Wei Gao.

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Gao, W., Farahani, M.R., Aslam, A. et al. Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Comput 20, 959–968 (2017). https://doi.org/10.1007/s10586-017-0887-3

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  • DOI: https://doi.org/10.1007/s10586-017-0887-3

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