Unevenness in Network Properties on the Social Semantic Web

Abstract

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. Using the SPARQL query language, a derived RDF graph can be constructed that is tailored to a specific question. After a brief introduction to the notion of unevenness, this methodology is applied to examine unevenness in network properties of semantic 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.