  In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been
published as noteworthy large, cross-domain, and freely available knowledge
graphs. Although extensively in use, these knowledge graphs are hard to compare
against each other in a given setting. Thus, it is a challenge for researchers
and developers to pick the best knowledge graph for their individual needs. In
our recent survey, we devised and applied data quality criteria to the
above-mentioned knowledge graphs. Furthermore, we proposed a framework for
finding the most suitable knowledge graph for a given setting. With this paper
we intend to ease the access to our in-depth survey by presenting simplified
rules that map individual data quality requirements to specific knowledge
graphs. However, this paper does not intend to replace our previously
introduced decision-support framework. For an informed decision on which KG is
best for you we still refer to our in-depth survey.
