During the last decade, entity embeddings have become ubiquitous in Artificial Intelligence. Such embeddings essentially serve as compact but semantically meaningful representations of the entities of interest. In most approaches, vectors are used for representing the entities themselves, as well as for representing their associated attributes. An important advantage of using attribute embeddings is that (some of the) semantic dependencies between the attributes can thus be captured. However, little is known about what kinds of semantic dependencies can be modelled in this way. The aim of this paper is to shed light on this question, focusing on settings where the embedding of an entity is obtained by pooling the embeddings of its known attributes. Our particular focus is on studying the theoretical limitations of different embedding strategies, rather than their ability to effectively learn attribute dependencies in practice. We first show a number of negative results, revealing that some of the most popular embedding models are not able to capture even basic Horn rules. However, we also find that some embedding strategies are capable, in principle, of modelling both monotonic and non-monotonic attribute dependencies.

翻译：在过去十年中,实体嵌入在人工智能中已变得无处不在。这种嵌入基本上是相关实体的缩入,但具有内在意义。在大多数方法中,矢量被用于代表实体本身,并代表其相关属性。使用属性嵌入的一个重要好处是,能够捕捉到属性之间的(某些)语义依赖性。然而,对于何种语义依赖性可以以这种方式建模却知之甚少。本文件的目的是阐明这一问题,侧重于一个实体的嵌入通过汇集其已知属性而获得的设置。我们特别侧重于研究不同嵌入战略的理论局限性,而不是它们在实践中有效学习属性依赖性的能力。我们首先显示一些负面的结果,表明一些最受欢迎的嵌入模式甚至无法捕捉到基本的霍恩规则。但我们也发现,一些嵌入战略原则上能够建模单调和非调属性依赖性。