  In this paper, we have explored the effects of different minibatch sampling
techniques in Knowledge Graph Completion. Knowledge Graph Completion (KGC) or
Link Prediction is the task of predicting missing facts in a knowledge graph.
KGC models are usually trained using margin, soft-margin or cross-entropy loss
function that promotes assigning a higher score or probability for true fact
triplets. Minibatch gradient descent is used to optimize these loss functions
for training the KGC models. But, as each minibatch consists of only a few
randomly sampled triplets from a large knowledge graph, any entity that occurs
in a minibatch, occurs only once in most cases. Because of this, these loss
functions ignore all other neighbors of any entity, whose embedding is being
updated at some minibatch step. In this paper, we propose a new random-walk
based minibatch sampling technique for training KGC models that optimizes the
loss incurred by a minibatch of closely connected subgraph of triplets instead
of randomly selected ones. We have shown results of experiments for different
models and datasets with our sampling technique and found that the proposed
sampling algorithm has varying effects on these datasets/models. Specifically,
we find that our proposed method achieves state-of-the-art performance on the
DB100K dataset.
