  Knowledge Graphs (KGs) have emerged as the de-facto standard for modeling and
querying datasets with a graph-like structure in the Semantic Web domain. Our
focus is on the performance challenges associated with querying KGs. We
developed three informationally equivalent JSON-based representations for KGs,
namely, Subject-based Name/Value (JSON-SNV), Documents of Triples (JSON-DT),
and Chain-based Name/Value (JSON-CNV). We analyzed the effects of these
representations on query performance by storing them on two prominent
document-based Data Management Systems (DMSs), namely, MongoDB and Couchbase
and executing a set of benchmark queries over them. We also compared the
execution times with row-store Virtuoso, column-store Virtuoso, and
\mbox{Blazegraph} as three major DMSs with different architectures (aka,
RDF-stores). Our results indicate that the representation type has a
significant performance impact on query execution. For instance, the JSON-SNV
outperforms others by nearly one order of magnitude to execute subject-subject
join queries. This and the other results presented in this paper can assist in
more accurate benchmarking of the emerging DMSs.
