A Probit Tensor Factorization Model For Relational Learning. Issue 3 (3rd July 2022)

• Record Type: Journal Article
• Authors: Liu, Ye; Song, Rui; Lu, Wenbin; Xiao, Yanghua
• Is Part Of: Journal of computational and graphical statistics. Volume 31:Issue 3(2022)
• Page Start: 846
• Page End: 855
• Publication Date: 2022-07-03
• Subjects: Alternating least square -- EM algorithm -- Link prediction -- Multi-relational data -- Open-world assumption -- Probit model; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2021.2003204
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: 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.