  Link prediction in large-scale knowledge graphs has gained increasing
attention recently. The OGB-LSC team presented OGB Large-Scale Challenge
(OGB-LSC), a collection of three real-world datasets for advancing the
state-of-the-art in large-scale graph machine learning. In this paper, we
introduce the solution of our team GraphMIRAcles in the WikiKG90M-LSC track of
OGB-LSC @ KDD Cup 2021. In the WikiKG90M-LSC track, the goal is to
automatically predict missing links in WikiKG90M, a large scale knowledge graph
extracted from Wikidata. To address this challenge, we propose a framework that
integrates three components -- a basic model ComplEx-CMRC, a rule miner AMIE 3,
and an inference model to predict missing links. Experiments demonstrate that
our solution achieves an MRR of 0.9707 on the test dataset. Moreover, as the
knowledge distillation in the inference model uses test tail candidates --
which are unavailable in practice -- we conduct ablation studies on knowledge
distillation. Experiments demonstrate that our model without knowledge
distillation achieves an MRR of 0.9533 on the full validation dataset.

Computation and Language;Machine Learning