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arXiv · 2211.12281

BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion

Alberto Cattaneo·Daniel Justus·Harry Mellor·Douglas Orr·Jerome Maloberti·Zhenying Liu·Thorin Farnsworth·Andrew Fitzgibbon·Blazej Banaszewski·Carlo Luschi
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Abstract

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.

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Alberto Cattaneo, Daniel Justus, Harry Mellor, Douglas Orr, Jerome Maloberti, Zhenying Liu, Thorin Farnsworth, Andrew Fitzgibbon, Blazej Banaszewski, Carlo Luschi. 2022-11-22. BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion. https://arxiv.org/abs/2211.12281

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