A Probit Tensor Factorization Model For Relational Learning
提出一种带Probit链接的二元张量分解模型，用于知识图谱中的链接预测，相比传统模型在预测准确性和可解释性上更有优势。
With the proliferation of knowledge graphs, modeling data with complex multi-relational 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, that is, 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 nonexisting 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. Supplementary files for this article are available online.