  State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep
neural networks trained with both false and true assertions: positive
assertions are explicitly taken from the knowledge base, whereas negative ones
are generated by random sampling of entities. In this paper, we argue that
random sampling is not a good training strategy since it is highly likely to
generate a huge number of nonsensical assertions during training, which does
not provide relevant training signal to the system. Hence, it slows down the
learning process and decreases accuracy. To address this issue, we propose an
alternative approach called Distributional Negative Sampling that generates
meaningful negative examples which are highly likely to be false. Our approach
achieves a significant improvement in Mean Reciprocal Rank values amongst two
different KBC algorithms in three standard academic benchmarks.
