  We present the award-winning submission to the WikiKG90Mv2 track of
OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge
graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution uses
a diverse ensemble of $85$ Knowledge Graph Embedding models combining five
different scoring functions (TransE, TransH, RotatE, DistMult, ComplEx) and two
different loss functions (log-sigmoid, sampled softmax cross-entropy). Each
individual model is trained in parallel on a Graphcore Bow Pod$_{16}$ using
BESS (Balanced Entity Sampling and Sharing), a new distribution framework for
KGE training and inference based on balanced collective communications between
workers. Our final model achieves a validation MRR of 0.2922 and a
test-challenge MRR of 0.2562, winning the first place in the competition. The
code is publicly available at:
https://github.com/graphcore/distributed-kge-poplar/tree/2022-ogb-submission.
