Our model borrows the idea of a translational scoring function from , which learns embeddings such that for a given valid triple t k ij = (e i , r k , e j ), the condition h i + g k ≈ h j holds, i.e., e j is the nearest neighbor of e i connected via relation r k .
we try to learn entity and relation embeddings to minimize the L1-norm dissimilarity measure given by d We train our model using hinge-loss which is given by the following expression where γ > 0 is a margin hyper-parameter, S is the set of valid triples, and S denotes the set of invalid triples, given formally as replace head entity ∪ {t k ij | e j ∈ E \ e j } replace tail entity Our model uses ConvKB (Nguyen et al., 2018) as a decoder.