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With the proliferation of knowledge graphs, modeling data with complexmultirelational structure has gained increasing attention in the area ofstatistical relational learning. One of the most important goals of statisticalrelational learning is link prediction, i.e., predicting whether certainrelations exist in the knowledge graph. A large number of models and algorithmshave been proposed to perform link prediction, among which tensor factorizationmethod has proven to achieve state-of-the-art performance in terms ofcomputation efficiency and prediction accuracy. However, a common drawback ofthe existing tensor factorization models is that the missing relations andnon-existing relations are treated in the same way, which results in a loss ofinformation. To address this issue, we propose a binary tensor factorizationmodel with probit link, which not only inherits the computation efficiency fromthe classic tensor factorization model but also accounts for the binary natureof relational data. Our proposed probit tensor factorization (PTF) model showsadvantages in both the prediction accuracy and interpretability

A Probit Tensor Factorization Model For Relational Learning