A Critical Examination of RESCAL for Completion of Knowledge Bases with Transitive Relations
This work addresses a critical gap in understanding model limitations for researchers in knowledge graph completion, though it is incremental as it focuses on analyzing an existing method.
The paper analyzes the RESCAL method for knowledge base completion and proves that it cannot encode asymmetric transitive relations, highlighting a fundamental limitation in its modeling capability.
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.