  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.
