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\section{Knowledge Graphs in Practice}  Rospocher, \textit{et al.} define present  knowledge graphs as collections of typically  time-invariant facts about entities, typically derived from structured data sources such as Freebase and  \cite{Rospocher2016}. They cite a dearth of event representations in current knowledge graphs as a shortcoming - limiting knowledge graphs to encyclopedic items such as birth and death dates - primarily due to the difficulty of obtaining temporal data about entities in a structured manner. Recent surveys such as those by Hogenboom, \textit{et al.} \cite{Hogenboom2016} and Deng, \textit{et al.} \cite{Deng2015} provide overviews of numerous methods for event extraction from a variety of sources including social media, news, academic publications, and even images and video, indicating that there is a great interest in finding ways to interpret and include such temporal data in a more structured format. Another review by Nickel \emph{et al.} explores machine learning methods for knowledge graphs, but limits their definition to directed labeled graphs, with the ability to optionally pre-define the schema.  They also review but do not take a position on the use of the closed versus open world assumptions.