The prize-winning paper deals with RDF datasets and the extraction of graph-based measures on RDF graphs. As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. The paper proposes two resources: (i) a software framework able to acquire, prepare, and perform a graphbased analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets from the LOD Cloud with values for 28 graph measures computed with the framework. A preliminary analysis based on the proposed resources and implications for synthetic dataset generators are introduced.
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A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs,
by Matthäus Zloch (GESIS), Maribel Acosta (KIT), Daniel Hienert (GESIS), Stefan Dietze (GESIS), and Stefan Conrad (HHU).