Making sense from Big RDF Data: OUSAF for measuring ontology usage
- Publication Type:
- Journal Article
- Software - Practice and Experience, 2015, 45 (8), pp. 1051 - 1071
- Issue Date:
|Ashraf_et_al-2015-Software-_Practice_and_Experience.pdf||Published Version||1.99 MB|
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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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