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Link prediction in large knowledge graphs has received a lot of attentionrecently because of its importance for inferring missing relations and forcompleting and improving noisily extracted knowledge graphs. Over the years anumber of machine learning researchers have presented various models forpredicting the presence of missing relations in a knowledge base. Although allthe previous methods are presented with empirical results that show highperformance on select datasets, there is almost no previous work onunderstanding the connection between properties of a knowledge base and theperformance of a model. In this paper we analyze the RESCAL method and provethat it can not encode asymmetric transitive relations in knowledge bases.

A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations