  In this paper, we propose a novel embedding model, named ConvKB, for
knowledge base completion. Our model ConvKB advances state-of-the-art models by
employing a convolutional neural network, so that it can capture global
relationships and transitional characteristics between entities and relations
in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity)
is represented as a 3-column matrix where each column vector represents a
triple element. This 3-column matrix is then fed to a convolution layer where
multiple filters are operated on the matrix to generate different feature maps.
These feature maps are then concatenated into a single feature vector
representing the input triple. The feature vector is multiplied with a weight
vector via a dot product to return a score. This score is then used to predict
whether the triple is valid or not. Experiments show that ConvKB achieves
better link prediction performance than previous state-of-the-art embedding
models on two benchmark datasets WN18RR and FB15k-237.
