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199834

Ontology Algorithm Using Two Classes of Regressions

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Last updated: 23 Jan 2023

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Abstract

Ontology similarity calculation is an important research topic in information retrieval and it is also widely used in computer science. In this paper, we propose new algorithms for ontology similarity measurement and ontology mapping using support vector regression and pseudo-Huber regression. Two experimental results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping.

DOI

10.21608/aeta.2013.199834

Keywords

ontology, ontology mapping, support vector regression, reproducing kernel Hilbert space,  -insensitive loss, Mercer kernel, regularization parameter, Huber loss function, Pseudo-Huber loss function

Authors

First Name

Yun

Last Name

Gao

MiddleName

-

Affiliation

Department of Editorial, Yunnan Normal University,Kunming, Yunnan 650092 China.

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First Name

Wei

Last Name

Gao

MiddleName

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Affiliation

2School of Information and technology, Yunnan Normal University, Kunming, Yunnan 650500, China

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Volume

2

Article Issue

1

Related Issue

28177

Issue Date

2013-01-01

Receive Date

2021-10-16

Publish Date

2013-01-01

Page Start

5

Page End

8

Print ISSN

2090-9535

Online ISSN

2090-9543

Article File

AETA_Volume 2_Issue 1_Pages 5-8.pdf

PDF . 340.5kB

Link

https://aeta.journals.ekb.eg/article_199834.html

Detail API

https://aeta.journals.ekb.eg/service?article_code=199834

Order

199,834

Type

Original Article

Type Code

2,017

Publication Type

Journal

Publication Title

Advanced Engineering Technology and Application

Publication Link

https://aeta.journals.ekb.eg/

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Article

Created At

23 Jan 2023