A hybrid concept similarity measure model for ontology environment

Dong, H; Hussain, FK; Chang, E

A hybrid concept similarity measure model for ontology environment

Dong, H Hussain, FK

Chang, E

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5872 LNCS pp. 848 - 857
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Dong, H	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Chang, E	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5872 LNCS pp. 848 - 857	en_US
dc.identifier.isbn	3642052894	en_US
dc.identifier.isbn	9783642052897	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30652
dc.description.abstract	In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology environment. Before the measure model is adopted, an ontology is required to be converted into a lightweight ontology space, and within it all the ontology concepts need to be transformed into the pseudo-concepts. By means of this model, ontology concept similarities are measured respectively based on the content of pseudo-concepts and the structure of the lightweight ontology space. Afterwards, the two aspects of concept similarity are leveraged as the eventual product. In addition, an experiment is conducted to evaluate the measure model based on a small ontology. Conclusions are drawn and future works are planned in the final section. © Springer-Verlag 2009.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-05290-3_103	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A hybrid concept similarity measure model for ontology environment	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5872 LNCS	en_US
dc.location.activity	Vilamoura, PORTUGAL
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5872 LNCS	en_US

Abstract:

In this paper, we present a hybrid concept similarity measure model for the ontology environment. Whilst to date many similar technologies have been developed for semantic networks, few of them can be directly applied to the semantic-rich ontology environment. Before the measure model is adopted, an ontology is required to be converted into a lightweight ontology space, and within it all the ontology concepts need to be transformed into the pseudo-concepts. By means of this model, ontology concept similarities are measured respectively based on the content of pseudo-concepts and the structure of the lightweight ontology space. Afterwards, the two aspects of concept similarity are leveraged as the eventual product. In addition, an experiment is conducted to evaluate the measure model based on a small ontology. Conclusions are drawn and future works are planned in the final section. © Springer-Verlag 2009.

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http://hdl.handle.net/10453/30652