Computationally efficient ontology selection in software requirement planning

Brown, RBK; Beydoun, G; Low, G; Tibben, W; Zamani, R; Garcia-Sanchez, F; Martínez-Béjar, R

Computationally efficient ontology selection in software requirement planning

Brown, RBK Beydoun, G

Low, G Tibben, W Zamani, R Garcia-Sanchez, F Martínez-Béjar, R

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Information Systems Frontiers, 2016, 18, (2), pp. 349-358
Issue Date:: 2016-04-01

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Field	Value	Language
dc.contributor.author	Brown, RBK
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445
dc.contributor.author	Low, G
dc.contributor.author	Tibben, W
dc.contributor.author	Zamani, R
dc.contributor.author	Garcia-Sanchez, F
dc.contributor.author	Martínez-Béjar, R
dc.date.accessioned	2022-01-04T01:04:33Z
dc.date.available	2022-01-04T01:04:33Z
dc.date.issued	2016-04-01
dc.identifier.citation	Information Systems Frontiers, 2016, 18, (2), pp. 349-358
dc.identifier.issn	1387-3326
dc.identifier.issn	1572-9419
dc.identifier.uri	http://hdl.handle.net/10453/152631
dc.description.abstract	Understanding the needs of stakeholders and prioritizing requirements are the vital steps in the development of any software application. Enabling tools to support these steps have a critical role in the success of the corresponding software application. Based on such a critical role, this paper presents a computationally efficient ontology selection in software requirement planning. The key point guiding the underlying design is that, once gathered, requirements need to be processed by decomposition towards the generation of a specified systems design. A representational framework allows for the expression of high level abstract conceptions under a single schema, which may then be made explicit in terms of axiomatic relations and expressed in a suitable ontology. The initial experimental results indicate that our framework for filtered selection of a suitable ontology operates in a computationally efficient manner.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Information Systems Frontiers
dc.relation.isbasedon	10.1007/s10796-014-9540-3
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Information Systems Frontiers, 2016, 18, (2), pp. 349-358]. The final authenticated version is available online at: [ https://link.springer.com/article/10.1007%2Fs10796-014-9540-3]”
dc.subject	0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Computationally efficient ontology selection in software requirement planning
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0806 Information Systems
utslib.for	0806 Information Systems
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-04T01:04:32Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	2

Abstract:

Understanding the needs of stakeholders and prioritizing requirements are the vital steps in the development of any software application. Enabling tools to support these steps have a critical role in the success of the corresponding software application. Based on such a critical role, this paper presents a computationally efficient ontology selection in software requirement planning. The key point guiding the underlying design is that, once gathered, requirements need to be processed by decomposition towards the generation of a specified systems design. A representational framework allows for the expression of high level abstract conceptions under a single schema, which may then be made explicit in terms of axiomatic relations and expressed in a suitable ontology. The initial experimental results indicate that our framework for filtered selection of a suitable ontology operates in a computationally efficient manner.

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http://hdl.handle.net/10453/152631