  Knowledge graphs link entities through relations to provide a structured
representation of real world facts. However, they are often incomplete, because
they are based on only a small fraction of all plausible facts. The task of
knowledge graph completion via link prediction aims to overcome this challenge
by inferring missing facts represented as links between entities. Current
approaches to link prediction leverage tensor factorization and/or deep
learning. Factorization methods train and deploy rapidly thanks to their small
number of parameters but have limited expressiveness due to their underlying
linear methodology. Deep learning methods are more expressive but also
computationally expensive and prone to overfitting due to their large number of
trainable parameters. We propose Neural Powered Tucker Network (NePTuNe), a new
hybrid link prediction model that couples the expressiveness of deep models
with the speed and size of linear models. We demonstrate that NePTuNe provides
state-of-the-art performance on the FB15K-237 dataset and near state-of-the-art
performance on the WN18RR dataset.
