deletions | additions       diff --git a/section_Knowledge_Graphs_in_Practice__.tex b/section_Knowledge_Graphs_in_Practice__.tex  index e522fe2..6e17682 100644  --- a/section_Knowledge_Graphs_in_Practice__.tex  +++ b/section_Knowledge_Graphs_in_Practice__.tex 
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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.  James \cite{james1992knowledge}, van de Riet and Meersman \cite{van1992knowledge},  Stokman and de Vries \cite{Stokman_1988}, and Zhang \cite{zhang2002knowledge}, present a formal theory of knowledge graphs as a specialization of semantic networks where meaning is expressed as structure, statements are unambiguous, and a limited set of relation types are used. These requirements also minimize redundancy within the knowledge graph, which simplifies analytical operations (including reasoning and queries).  Popping explores the use of knowledge graphs and their challenges at the time in their use in network text analysis \cite{Popping_2003}.   Popping defines knowledge graphs similarly to Zhang, stating that they are a type of semantic network that uses only a few types of relations, but also states that additional knowledge can be added to the graph.