  Translation-based embedding models have gained significant attention in link
prediction tasks for knowledge graphs. TransE is the primary model among
translation-based embeddings and is well-known for its low complexity and high
efficiency. Therefore, most of the earlier works have modified the score
function of the TransE approach in order to improve the performance of link
prediction tasks. Nevertheless, proven theoretically and experimentally, the
performance of TransE strongly depends on the loss function. Margin Ranking
Loss (MRL) has been one of the earlier loss functions which is widely used for
training TransE. However, the scores of positive triples are not necessarily
enforced to be sufficiently small to fulfill the translation from head to tail
by using relation vector (original assumption of TransE). To tackle this
problem, several loss functions have been proposed recently by adding upper
bounds and lower bounds to the scores of positive and negative samples.
Although highly effective, previously developed models suffer from an expansion
in search space for a selection of the hyperparameters (in particular the upper
and lower bounds of scores) on which the performance of the translation-based
models is highly dependent. In this paper, we propose a new loss function
dubbed Adaptive Margin Loss (AML) for training translation-based embedding
models. The formulation of the proposed loss function enables an adaptive and
automated adjustment of the margin during the learning process. Therefore,
instead of obtaining two values (upper bound and lower bound), only the center
of a margin needs to be determined. During learning, the margin is expanded
automatically until it converges. In our experiments on a set of standard
benchmark datasets including Freebase and WordNet, the effectiveness of AML is
confirmed for training TransE on link prediction tasks.
