Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph
  Embeddings
  Among the top approaches of recent years, link prediction using knowledge
graph embedding (KGE) models has gained significant attention for knowledge
graph completion. Various embedding models have been proposed so far, among
which, some recent KGE models obtain state-of-the-art performance on link
prediction tasks by using embeddings with a high dimension (e.g. 1000) which
accelerate the costs of training and evaluation considering the large scale of
KGs. In this paper, we propose a simple but effective performance boosting
strategy for KGE models by using multiple low dimensions in different
repetition rounds of the same model. For example, instead of training a model
one time with a large embedding size of 1200, we repeat the training of the
model 6 times in parallel with an embedding size of 200 and then combine the 6
separate models for testing while the overall numbers of adjustable parameters
are same (6*200=1200) and the total memory footprint remains the same. We show
that our approach enables different models to better cope with their
expressiveness issues on modeling various graph patterns such as symmetric,
1-n, n-1 and n-n. In order to justify our findings, we conduct experiments on
various KGE models. Experimental results on standard benchmark datasets, namely
FB15K, FB15K-237 and WN18RR, show that multiple low-dimensional models of the
same kind outperform the corresponding single high-dimensional models on link
prediction in a certain range and have advantages in training efficiency by
using parallel training while the overall numbers of adjustable parameters are
same.
