  As the role of knowledge-based systems in IoT keeps growing, ensuring
resource efficiency of RDF stores becomes critical. However, up until now
benchmarks of RDF stores were most often conducted with only one dataset, and
the differences between the datasets were not explored in detail. In this paper
we aim to close this research gap by experimentally evaluating load times of
eight diverse RDF datasets from the RiverBench benchmark suite. In the
experiments we use five different RDF store implementations and several
resource-constrained hardware platforms. To analyze the results, we introduce
the notion of relative loading speed (RLS), allowing us to observe that the
loading speed can differ between datasets by as much as a factor of 9.01. This
serves as clear evidence that "not all RDF is created equal" and stresses the
importance of using multiple benchmark datasets in evaluations. We outline the
possible reasons for this drastic difference, which should be further
investigated in future work. To this end, we published the data, code, and the
results of our experiments.
