A Probit Tensor Factorization Model For Relational Learning
This work provides an incremental improvement for researchers and practitioners in statistical relational learning by enhancing tensor factorization methods for binary relational data.
The paper tackles the problem of link prediction in knowledge graphs by addressing the issue where existing tensor factorization models treat missing and non-existing relations identically, leading to information loss. The proposed probit tensor factorization model improves prediction accuracy and interpretability while maintaining computational efficiency.
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