  Indexing the Web of Data offers many opportunities, in particular, to find
and explore data sources. One major design decision when indexing the Web of
Data is to find a suitable index model, i.e., how to index and summarize data.
Various efforts have been conducted to develop specific index models for a
given task. With each index model designed, implemented, and evaluated
independently, it remains difficult to judge whether an approach generalizes
well to another task, set of queries, or dataset. In this work, we empirically
evaluate six representative index models with unique feature combinations.
Among them is a new index model incorporating inferencing over RDFS and
owl:sameAs. We implement all index models for the first time into a single,
stream-based framework. We evaluate variations of the index models considering
sub-graphs of size 0, 1, and 2 hops on two large, real-world datasets. We
evaluate the quality of the indices regarding the compression ratio,
summarization ratio, and F1-score denoting the approximation quality of the
stream-based index computation. The experiments reveal huge variations in
compression ratio, summarization ratio, and approximation quality for different
index models, queries, and datasets. However, we observe meaningful
correlations in the results that help to determine the right index model for a
given task, type of query, and dataset.
