  Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the
development of efficient tools for allowing end users to extract useful
insights from these sources of knowledge. In such a context, reducing the size
of a Resource Description Framework (RDF) graph while preserving all
information can speed up query engines by limiting data shuffle, especially in
a distributed setting. This paper presents two algorithms for RDF graph
summarization: Grouping Based Summarization (GBS) and Query Based Summarization
(QBS). The latter is an optimized and lossless approach for the former method.
We empirically study the effectiveness of the proposed lossless RDF graph
summarization to retrieve complete data, by rewriting an RDF Query Language
called SPARQL query with fewer triple patterns using a semantic similarity. We
conduct our experimental study in instances of four datasets with different
sizes. Compared with the state-of-the-art query engine Sparklify executed over
the original RDF graphs as a baseline, QBS query execution time is reduced by
up to 80% and the summarized RDF graph is decreased by up to 99%.
