  We study the problem of inducing interpretability in KG embeddings.
Specifically, we explore the Universal Schema (Riedel et al., 2013) and propose
a method to induce interpretability. There have been many vector space models
proposed for the problem, however, most of these methods don't address the
interpretability (semantics) of individual dimensions. In this work, we study
this problem and propose a method for inducing interpretability in KG
embeddings using entity co-occurrence statistics. The proposed method
significantly improves the interpretability, while maintaining comparable
performance in other KG tasks.
