  Knowledge Graph Embedding (KGE) is a popular approach, which aims to
represent entities and relations of a knowledge graph in latent spaces. Their
representations are known as embeddings. To measure the plausibility of
triplets, score functions are defined over embedding spaces. Despite wide
dissemination of KGE in various tasks, KGE methods have limitations in
reasoning abilities. In this paper we propose a mathematical framework to
compare reasoning abilities of KGE methods. We show that STransE has a higher
capability than TransComplEx, and then present new STransCoRe method, which
improves the STransE by combining it with the TransCoRe insights, which can
reduce the STransE space complexity.
