  Knowledge graph completion refers to predicting missing triples. Most
approaches achieve this goal by predicting entities, given an entity and a
relation. We predict missing triples via the relation prediction. To this end,
we frame the relation prediction problem as a multi-label classification
problem and propose a shallow neural model (SHALLOM) that accurately infers
missing relations from entities. SHALLOM is analogous to C-BOW as both
approaches predict a central token (p) given surrounding tokens ((s,o)). Our
experiments indicate that SHALLOM outperforms state-of-the-art approaches on
the FB15K-237 and WN18RR with margins of up to $3\%$ and $8\%$ (absolute),
respectively, while requiring a maximum training time of 8 minutes on these
datasets. We ensure the reproducibility of our results by providing an
open-source implementation including training and evaluation scripts at
{\url{https://github.com/dice-group/Shallom}.}
