  Knowledge graphs contain knowledge about the world and provide a structured
representation of this knowledge. Current knowledge graphs contain only a small
subset of what is true in the world. Link prediction approaches aim at
predicting new links for a knowledge graph given the existing links among the
entities. Tensor factorization approaches have proved promising for such link
prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is
among the first tensor factorization approaches. CP generally performs poorly
for link prediction as it learns two independent embedding vectors for each
entity, whereas they are really tied. We present a simple enhancement of CP
(which we call SimplE) to allow the two embeddings of each entity to be learned
dependently. The complexity of SimplE grows linearly with the size of
embeddings. The embeddings learned through SimplE are interpretable, and
certain types of background knowledge can be incorporated into these embeddings
through weight tying. We prove SimplE is fully expressive and derive a bound on
the size of its embeddings for full expressivity. We show empirically that,
despite its simplicity, SimplE outperforms several state-of-the-art tensor
factorization techniques. SimplE's code is available on GitHub at
https://github.com/Mehran-k/SimplE.

Machine Learning;Machine Learning