  Knowledge graphs (KGs) represent world's facts in structured forms. KG
completion exploits the existing facts in a KG to discover new ones.
Translation-based embedding model (TransE) is a prominent formulation to do KG
completion. Despite the efficiency of TransE in memory and time, it suffers
from several limitations in encoding relation patterns such as symmetric,
reflexive etc. To resolve this problem, most of the attempts have circled
around the revision of the score function of TransE i.e., proposing a more
complicated score function such as Trans(A, D, G, H, R, etc) to mitigate the
limitations. In this paper, we tackle this problem from a different
perspective. We show that existing theories corresponding to the limitations of
TransE are inaccurate because they ignore the effect of loss function.
Accordingly, we pose theoretical investigations of the main limitations of
TransE in the light of loss function. To the best of our knowledge, this has
not been investigated so far comprehensively. We show that by a proper
selection of the loss function for training the TransE model, the main
limitations of the model are mitigated. This is explained by setting
upper-bound for the scores of positive samples, showing the region of truth
(i.e., the region that a triple is considered positive by the model). Our
theoretical proofs with experimental results fill the gap between the
capability of translation-based class of embedding models and the loss
function. The theories emphasise the importance of the selection of the loss
functions for training the models. Our experimental evaluations on different
loss functions used for training the models justify our theoretical proofs and
confirm the importance of the loss functions on the performance.
