Unevenness in Network Properties on the Social Semantic Web
نویسنده
چکیده
This paper studies unevenness in network properties on the social Semantic Web. First, we propose a two-step methodology for processing and analyzing social network data from the Semantic Web, based on the SPARQL query language. After a brief introduction to the notion of unevenness, the methodology is applied to examine unevenness in network properties of real-world data. Comparing Lorenz curves for different centrality measures, it is shown how examinations of unevenness can provide crucial hints regarding the topology of (social) Semantic Web data.
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تاریخ انتشار 2008