  Learning low-dimensional representations for entities and relations in
knowledge graphs using contrastive estimation represents a scalable and
effective method for inferring connectivity patterns. A crucial aspect of
contrastive learning approaches is the choice of corruption distribution that
generates hard negative samples, which force the embedding model to learn
discriminative representations and find critical characteristics of observed
data. While earlier methods either employ too simple corruption distributions,
i.e. uniform, yielding easy uninformative negatives or sophisticated
adversarial distributions with challenging optimization schemes, they do not
explicitly incorporate known graph structure resulting in suboptimal negatives.
In this paper, we propose Structure Aware Negative Sampling (SANS), an
inexpensive negative sampling strategy that utilizes the rich graph structure
by selecting negative samples from a node's k-hop neighborhood. Empirically, we
demonstrate that SANS finds semantically meaningful negatives and is
competitive with SOTA approaches while requires no additional parameters nor
difficult adversarial optimization.
