Making sense from Big RDF Data: OUSAF for measuring ontology usage

Ashraf, J; Hussain, OK; Hussain, FK

Making sense from Big RDF Data: OUSAF for measuring ontology usage

Ashraf, J Hussain, OK Hussain, FK

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Publication Type:: Journal Article
Citation:: Software - Practice and Experience, 2015, 45 (8), pp. 1051 - 1071
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Ashraf, J	en_US
dc.contributor.author	Hussain, OK	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Software - Practice and Experience, 2015, 45 (8), pp. 1051 - 1071	en_US
dc.identifier.issn	0038-0644	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121545
dc.description.abstract	Copyright © 2014 John Wiley & Sons, Ltd. Summary Recent growth and advancements in the Semantic Web have shifted the research focus from being knowledge-centered to data-centered. This has led to the increased use of ontologies to structurally represent the data, thereby generating huge amounts of RDF data, which we term Big RDF Data. Nevertheless, the literature lacks the tools to analyze Big RDF Data and make sense of it. Access to such tools would enable pragmatic inputs and insights for users in respect of such tasks as the usage and adoption of Ontologies, their uptake by different users in the community, and the identification of prevalent patterns. This analysis, which we term Ontology Usage, is important from the viewpoint of users who need informed inputs in the various stages of the ontology engineering lifecycle, such as ontology evolution, ontology population, and ontology deployment. In this paper, we propose the Ontology USage Analysis Framework (OUSAF), which performs analysis of Ontology Usage on Big RDF Data and synthesizes the usage knowledge acquired. OUSAF provides a methodological approach to performing the various phases such as identifying, analyzing, representing, and utilizing the Ontology usage results from Big RDF Data. We describe in detail each of those phases and the metrics required to perform the analysis of each phase. The utilization of the OUSAF results obtained by users such as data publishers and ontology developers is demonstrated by a dataset collected in the e-business domain.	en_US
dc.relation.ispartof	Software - Practice and Experience	en_US
dc.relation.isbasedon	10.1002/spe.2272	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Making sense from Big RDF Data: OUSAF for measuring ontology usage	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	45	en_US
utslib.for	080301 Bioinformatics Software	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
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.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

Copyright © 2014 John Wiley & Sons, Ltd. Summary Recent growth and advancements in the Semantic Web have shifted the research focus from being knowledge-centered to data-centered. This has led to the increased use of ontologies to structurally represent the data, thereby generating huge amounts of RDF data, which we term Big RDF Data. Nevertheless, the literature lacks the tools to analyze Big RDF Data and make sense of it. Access to such tools would enable pragmatic inputs and insights for users in respect of such tasks as the usage and adoption of Ontologies, their uptake by different users in the community, and the identification of prevalent patterns. This analysis, which we term Ontology Usage, is important from the viewpoint of users who need informed inputs in the various stages of the ontology engineering lifecycle, such as ontology evolution, ontology population, and ontology deployment. In this paper, we propose the Ontology USage Analysis Framework (OUSAF), which performs analysis of Ontology Usage on Big RDF Data and synthesizes the usage knowledge acquired. OUSAF provides a methodological approach to performing the various phases such as identifying, analyzing, representing, and utilizing the Ontology usage results from Big RDF Data. We describe in detail each of those phases and the metrics required to perform the analysis of each phase. The utilization of the OUSAF results obtained by users such as data publishers and ontology developers is demonstrated by a dataset collected in the e-business domain.

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http://hdl.handle.net/10453/121545