  Over the past decade, knowledge graphs became popular for capturing
structured domain knowledge. Relational learning models enable the prediction
of missing links inside knowledge graphs. More specifically, latent distance
approaches model the relationships among entities via a distance between latent
representations. Translating embedding models (e.g., TransE) are among the most
popular latent distance approaches which use one distance function to learn
multiple relation patterns. However, they are mostly inefficient in capturing
symmetric relations since the representation vector norm for all the symmetric
relations becomes equal to zero. They also lose information when learning
relations with reflexive patterns since they become symmetric and transitive.
We propose the Multiple Distance Embedding model (MDE) that addresses these
limitations and a framework to collaboratively combine variant latent
distance-based terms. Our solution is based on two principles: 1) we use a
limit-based loss instead of a margin ranking loss and, 2) by learning
independent embedding vectors for each of the terms we can collectively train
and predict using contradicting distance terms. We further demonstrate that MDE
allows modeling relations with (anti)symmetry, inversion, and composition
patterns. We propose MDE as a neural network model that allows us to map
non-linear relations between the embedding vectors and the expected output of
the score function. Our empirical results show that MDE performs competitively
to state-of-the-art embedding models on several benchmark datasets.
