Semantic Web 12 (2021) 789–812 789 DOI 10.3233/SW-200409 IOS Press Charaterizing RDF graphs through graph-based measures – framework and assessment Matthäus Zloch a,c,*, Maribel Acosta b,d, Daniel Hienert a, Stefan Conrad c and Stefan Dietze a,c a GESIS – Leibniz-Institute for the Social Sciences, Cologne, Germany E-mails:
[email protected],
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[email protected] b Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany E-mail:
[email protected] c Institute DBS, Heinrich-Heine University, Düsseldorf, Germany E-mails:
[email protected],
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[email protected] d Center of Computer Science, Ruhr-University Bochum, Bochum, Germany E-mail:
[email protected] Editor: Aidan Hogan, Universidad de Chile, Chile Solicited reviews: Gëzim Sejdiu, University of Bonn, Germany; Michael Röder, Paderborn University, Germany Abstract. The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs.