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SEKI@home is a crowd-sourced knowledge graph that is aggregated from multiple sources \cite{steiner2012seki}.  This project maintains entity-level provenance using the PROV Ontology \cite{Moreau_2015}.  The project has also incorporated real-time matching against news articles \cite{steiner_iswc_2012}.  The Knowledge Vault handles knowledge graph uncertainty as a result of automated fact extraction from web Web  pages \cite{Dong_2014}. DBPedia is a large-scale transformation of Wikipedia into a knowledge graph \cite{Bizer_2009}.  It uses a mostly fixed schema and provides provenance of which wikipedia pages each entity was derived from.  A number of biomedical knowledge graphs have been constructed from public databases, including Bio2RDF\cite{Callahan_2013}, Neurocommons \cite{Ruttenberg_2009}, and LinkedLifeData \cite{momtchev2009expanding}.  All three knowledge graphs provide dataset-level provenance.  \subsection{Commercial Knowledge Graphs}  Freebase is a knowledge graph that is open to public access and curation \cite{Bollacker_2008} and has become the basis for the Google Knowledge Graph, which has extended it with knowledge gleaned from their regular search engine crawls of the web Web  \cite{singhal2012introducing}. Monteiro and Moura \cite{10110943220141101} present a thoughtful analysis of the role of the Google Knowledge Graph as a realization of the Semantic Web vision \cite{bernerslee2000semantic} as Web 4.0, and show how it merges rule-oriented semantic analysis with statistical predictive approaches.  Microsoft has also introduced a knowledge graph called ``Satori'' to enhance Bing search results \cite{qian2013understand}.