A Probit Tensor Factorization Model For Relational Learning

TL;DR

This paper introduces a probit tensor factorization model for relational learning that improves link prediction accuracy and interpretability by better modeling the binary nature of relational data in knowledge graphs.

Contribution

It proposes a novel probit tensor factorization model that enhances existing tensor methods by explicitly handling binary relational data, improving accuracy and interpretability.

Findings

Achieves state-of-the-art prediction accuracy

Offers improved interpretability over existing models

Maintains computational efficiency

Abstract

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