  There is growing interest in the use of Knowledge Graphs (KGs) for the
representation, exchange, and reuse of scientific data. While KGs offer the
prospect of improving the infrastructure for working with scalable and reusable
scholarly data consistent with the FAIR (Findability, Accessibility,
Interoperability, and Reusability) principles, the state-of-the-art Data
Management Systems (DMSs) for processing large KGs leave somewhat to be
desired. In this paper, we studied the performance of some of the major DMSs in
the context of querying KGs with the goal of providing a finely-grained,
comparative analysis of DMSs representing each of the four major DMS types. We
experimented with four well-known scientific KGs, namely, Allie, Cellcycle,
DrugBank, and LinkedSPL against Virtuoso, Blazegraph, RDF-3X, and MongoDB as
the representative DMSs. Our results suggest that the DMSs display limitations
in processing complex queries on the KG datasets. Depending on the query type,
the performance differentials can be several orders of magnitude. Also, no
single DMS appears to offer consistently superior performance. We present an
analysis of the underlying issues and outline two integrated approaches and
proposals for resolving the problem.
