  As Knowledge Graphs (KGs) continue to gain widespread momentum for use in
different domains, storing the relevant KG content and efficiently executing
queries over them are becoming increasingly important. A range of Data
Management Systems (DMSs) have been employed to process KGs. This paper aims to
provide an in-depth analysis of query performance across diverse DMSs and KG
query types. Our aim is to provide a fine-grained, comparative analysis of four
major DMS types, namely, row-, column-, graph-, and document-stores, against
major query types, namely, subject-subject, subject-object, tree-like, and
optional joins. In particular, we analyzed the performance of row-store
Virtuoso, column-store Virtuoso, Blazegraph (i.e., graph-store), and MongoDB
(i.e., document-store) using five well-known benchmarks, namely, BSBM, WatDiv,
FishMark, BowlognaBench, and BioBench-Allie. Our results show that no single
DMS displays superior query performance across the four query types. In
particular, row- and column-store Virtuoso are a factor of 3-8 faster for
tree-like joins, Blazegraph performs around one order of magnitude faster for
subject-object joins, and MongoDB performs over one order of magnitude faster
for high-selective queries.
