  Link prediction in large knowledge graphs has received a lot of attention
recently because of its importance for inferring missing relations and for
completing and improving noisily extracted knowledge graphs. Over the years a
number of machine learning researchers have presented various models for
predicting the presence of missing relations in a knowledge base. Although all
the previous methods are presented with empirical results that show high
performance on select datasets, there is almost no previous work on
understanding the connection between properties of a knowledge base and the
performance of a model. In this paper we analyze the RESCAL method and prove
that it can not encode asymmetric transitive relations in knowledge bases.
