Research on knowledge graph embeddings has recently evolved into knowledge base embeddings, where the goal is not only to map facts into vector spaces but also constrain the models so that they take into account the relevant conceptual knowledge available. This paper examines recent methods that have been proposed to embed knowledge bases in description logic into vector spaces through the lens of their geometric-based semantics. We identify several relevant theoretical properties, which we draw from the literature and sometimes generalize or unify. We then investigate how concrete embedding methods fit in this theoretical framework.

翻译：知识图谱嵌入的研究近期已发展为知识库嵌入，其目标不仅在于将事实映射至向量空间，同时约束模型以纳入可用的相关概念知识。本文通过基于几何的语义学视角，考察了近期提出的将描述逻辑知识库嵌入向量空间的方法。我们识别出若干从文献中提取、有时加以推广或统一的相关理论特性，进而探究具体嵌入方法如何融入这一理论框架。