In this paper we propose and study the novel problem of explaining node
embeddings by finding embedded human interpretable subspaces in already trained
unsupervised node representation embeddings. We use an external knowledge base
that is organized as a taxonomy of human-understandable concepts over entities
as a guide to identify subspaces in node embeddings learned from an entity
graph derived from Wikipedia. We propose a method that given a concept finds a
linear transformation to a subspace where the structure of the concept is
retained. Our initial experiments show that we obtain low error in finding
fine-grained concepts.

本文提出和研究了一个新问题，即通过在已经训练好的无监督节点表示嵌入中找到嵌入的人类可解释子空间来解释节点嵌入。我们使用一个人类可理解的概念组成的分类知识库作为指南，在从维基百科中导出的实体图上学习的节点嵌入中识别子空间。我们提出了一种方法，通过给定一个概念，找到一个线性变换到子空间，以保留概念的结构。我们的初步实验表明，我们在找到精细概念方面获得了较低的误差。