  With the proliferation of knowledge graphs, modeling data with complex
multirelational structure has gained increasing attention in the area of
statistical relational learning. One of the most important goals of statistical
relational learning is link prediction, i.e., predicting whether certain
relations exist in the knowledge graph. A large number of models and algorithms
have been proposed to perform link prediction, among which tensor factorization
method has proven to achieve state-of-the-art performance in terms of
computation efficiency and prediction accuracy. However, a common drawback of
the existing tensor factorization models is that the missing relations and
non-existing relations are treated in the same way, which results in a loss of
information. To address this issue, we propose a binary tensor factorization
model with probit link, which not only inherits the computation efficiency from
the classic tensor factorization model but also accounts for the binary nature
of relational data. Our proposed probit tensor factorization (PTF) model shows
advantages in both the prediction accuracy and interpretability
