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Cluster Computing

Erschienen in:

03.05.2017

Distance learning techniques for ontology similarity measuring and ontology mapping

verfasst von: Wei Gao, Mohammad Reza Farahani, Adnan Aslam, Sunilkumar Hosamani

Erschienen in: Cluster Computing | Ausgabe 2/2017

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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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Titel: Distance learning techniques for ontology similarity measuring and ontology mapping
verfasst von: Wei Gao
Mohammad Reza Farahani
Adnan Aslam
Sunilkumar Hosamani
Publikationsdatum: 03.05.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2017
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-0887-3

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