  Knowledge Graphs (KG) are gaining increasing attention in both academia and
industry. Despite their diverse benefits, recent research have identified
social and cultural biases embedded in the representations learned from KGs.
Such biases can have detrimental consequences on different population and
minority groups as applications of KG begin to intersect and interact with
social spheres. This paper aims at identifying and mitigating such biases in
Knowledge Graph (KG) embeddings. As a first step, we explore popularity bias --
the relationship between node popularity and link prediction accuracy. In case
of node2vec graph embeddings, we find that prediction accuracy of the embedding
is negatively correlated with the degree of the node. However, in case of
knowledge-graph embeddings (KGE), we observe an opposite trend. As a second
step, we explore gender bias in KGE, and a careful examination of popular KGE
algorithms suggest that sensitive attribute like the gender of a person can be
predicted from the embedding. This implies that such biases in popular KGs is
captured by the structural properties of the embedding. As a preliminary
solution to debiasing KGs, we introduce a novel framework to filter out the
sensitive attribute information from the KG embeddings, which we call FAN
(Filtering Adversarial Network). We also suggest the applicability of FAN for
debiasing other network embeddings which could be explored in future work.
