  Knowledge Graph (KG) completion is an important task that greatly benefits
knowledge discovery in many fields (e.g. biomedical research). In recent years,
learning KG embeddings to perform this task has received considerable
attention. Despite the success of KG embedding methods, they predominantly use
negative sampling, resulting in increased computational complexity as well as
biased predictions due to the closed world assumption. To overcome these
limitations, we propose \textbf{KG-NSF}, a negative sampling-free framework for
learning KG embeddings based on the cross-correlation matrices of embedding
vectors. It is shown that the proposed method achieves comparable link
prediction performance to negative sampling-based methods while converging much
faster.
