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dc.contributor.authorDong, Hai
dc.contributor.authorHussain, Farookh Khadeer
dc.contributor.authorChang, Elizabeth
dc.contributor.editorRobert Meersman
dc.contributor.editorPilar Herrero
dc.contributor.editorTharam Dillon
dc.date.accessioned2017-01-30T10:43:14Z
dc.date.available2017-01-30T10:43:14Z
dc.date.created2010-03-29T20:04:21Z
dc.date.issued2009
dc.identifier.citationDong, Hai and Hussain, Farookh Khadeer and Chang, Elizabeth. 2009. A hybrid concept similarity measure model for ontology environment, in Robert Meersman, Pilar Herrero and Tharam Dillon (ed), On the move to meaningful internet systems: OTM 2009 workshops. pp. 848-857. Heidelberg: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/5012
dc.identifier.doi10.1007/978-3-642-05290-3_103
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.

dc.publisherSpringer
dc.subjectsemantic similarity models
dc.subjectontology
dc.subjectlatent semantic indexing
dc.subjectconcept similarity measure
dc.titleA hybrid concept similarity measure model for ontology environment
dc.typeBook Chapter
dcterms.source.startPage848
dcterms.source.endPage857
dcterms.source.titleOn the move to meaningful internet systems: OTM 2009 workshops
dcterms.source.isbn9783642052897
dcterms.source.placeHeidelberg
dcterms.source.chapter112
curtin.note

The original publication is available at : http://www.springerlink.com

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available
curtin.facultyCurtin Business School
curtin.facultyThe Digital Ecosystems and Business Intelligence Institute (DEBII)


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