Abstract:
RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge
Bases (KBs) representing as close as possible the actual contents of the KB both in terms of structure and data. RDF Summarization
allows for better exploration and visualization of the underlying RDF graphs, optimization of queries or query evaluation
in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature,
there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different
results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality and bestfitness
for specific tasks, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality
Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of
the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary of the KB that
could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing
how close the proposed summary is to the ideal solution (when this is available) by defining and computing its precision, recall
and F-measure against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can
be retrieved) and in what degree by the proposed summary. Again we define and compute its precision, recall and F-measure
against the data contained in the original KB. We also compute the connectivity of the proposed summary compared to the ideal
one, since in many cases (like, e.g., when we want to query) this is an important factor and in general in RDF, datasets that are
linked within are usually used. We use our quality framework to test the results of three of the best RDF Graph Summarization
algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes
and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the
suitability of the proposed quality framework in order to get useful insights for the quality of the presented results.
