  Tensor factorization based models have shown great power in knowledge graph
completion (KGC). However, their performance usually suffers from the
overfitting problem seriously. This motivates various regularizers -- such as
the squared Frobenius norm and tensor nuclear norm regularizers -- while the
limited applicability significantly limits their practical usage. To address
this challenge, we propose a novel regularizer -- namely, DUality-induced
RegulArizer (DURA) -- which is not only effective in improving the performance
of existing models but widely applicable to various methods. The major novelty
of DURA is based on the observation that, for an existing tensor factorization
based KGC model (primal), there is often another distance based KGC model
(dual) closely associated with it. Experiments show that DURA yields consistent
and significant improvements on benchmarks.
