Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be rega...

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Bibliographic Details
Main Authors:	Linli Zhu, Gang Hua
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	Small ontology multi-dividing ontology learning similarity measure
Online Access:	https://ieeexplore.ieee.org/document/9276418/

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spelling	doaj-d888a797ca0a4c3fa89faa33a10f63ad2021-03-30T03:50:43ZengIEEEIEEE Access2169-35362020-01-01822070322070910.1109/ACCESS.2020.30416599276418Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample SettingLinli Zhu0https://orcid.org/0000-0001-7044-4860Gang Hua1https://orcid.org/0000-0001-7547-7143School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaThe multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of U-statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications.https://ieeexplore.ieee.org/document/9276418/Small ontologymulti-dividing ontology learningsimilarity measure
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Linli Zhu Gang Hua
spellingShingle	Linli Zhu Gang Hua Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting IEEE Access Small ontology multi-dividing ontology learning similarity measure
author_facet	Linli Zhu Gang Hua
author_sort	Linli Zhu
title	Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting
title_short	Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting
title_full	Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting
title_fullStr	Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting
title_full_unstemmed	Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting
title_sort	theoretical perspective of multi-dividing ontology learning trick in two-sample setting
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2020-01-01
description	The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of U-statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications.
topic	Small ontology multi-dividing ontology learning similarity measure
url	https://ieeexplore.ieee.org/document/9276418/
work_keys_str_mv	AT linlizhu theoreticalperspectiveofmultidividingontologylearningtrickintwosamplesetting AT ganghua theoreticalperspectiveofmultidividingontologylearningtrickintwosamplesetting
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Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

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