  In this paper, we study the problem of learning continuous vector
representations of knowledge graphs for predicting missing links. We present a
new approach called ConEx, which infers missing links by leveraging the
composition of a 2D convolution with a Hermitian inner product of
complex-valued embedding vectors. We evaluate ConEx against state-of-the-art
approaches on the WN18RR, FB15K-237, KINSHIP and UMLS benchmark datasets. Our
experimental results show that ConEx achieves a performance superior to that of
state-of-the-art approaches such as RotatE, QuatE and TuckER on the link
prediction task on all datasets while requiring at least 8 times fewer
parameters. We ensure the reproducibility of our results by providing an
open-source implementation which includes the training, evaluation scripts
along with pre-trained models at https://github.com/conex-kge/ConEx.
