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\section{Conclusions}  Knowledge graphs are a critical component of the Semantic Web and serve as information hubs for general use as well as for domain-specific applications.  Most knowledge graphs seek to aggregate knowledge from third party sources, whether from external databases, from data aggregated though  crawling the Web, or using through the application of  entity and relationship extraction techiniques. methods.  Knowledge graphs are not simply aggregations of RDF or linked data, but instead specifically focus on critically provide  time-invariant information about entities of general interest. They Their structures  tend to rely be focused  on a limited set of relations and try to adhere adhering  to a coherent knowledge model.  This sets model, setting  them apart from the linked data cloud in general, which usually has relied on the open framework of the Semantic Web to provide accommodate a  completely free-form use of vocabularies and ontologies. While Although  some knowledge graphs track the provenance of their content, it rigorous provenance  is by no means a universal practice. characteristic.  We argue that, instead, that  knowledge graphs should always provide prioritize  the epistemology(how we know what we know)  of the knowledge it contains, contains -- how we know what we know --  and that Nanopublications are a suitable framework in which to do so. Semantic publishing that does not provide a level of statement epistemology can be considered ``Bare Statement'' graphs.  Since so many knowledge graphs are curated from third parties, and because of the nature of publishing on the Web (Anyone (\textit{Anyone}  can say Anything \textit{Anything}  about Any \textit{Any}  subject), as knowledge graphs increase in popularity it will become critical to avoid use of such ``Bare Statement'' graphs.