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.

翻译：知识图谱嵌入（KGE）是一种流行的方法，旨在将知识图谱的实体和关系表示在潜在空间中。其表示形式称为嵌入。为了衡量三元组的合理性，在嵌入空间上定义了评分函数。尽管KGE在各种任务中得到了广泛传播，但KGE方法在推理能力方面存在局限性。本文提出了一个数学框架来比较KGE方法的推理能力。我们证明了STransE比TransComplEx具有更高的能力，然后提出了新的STransCoRe方法，该方法通过结合TransCoRe的洞见改进了STransE，从而可以降低STransE的空间复杂度。