A Logistic Additive Approach for Relation Prediction in Multi-relational Data

This paper introduces a new stepwise approach for predicting one specific binary relationship in a multi-relational setting. The approach includes a phase of initializing the components of a logistic additive model by matrix factorization and a phase of further optimizing the components with an additive restriction and the Bernoulli modelling assumption. By using low-rank approximations on a set of matrices derived from various interactions of the multi-relational data, the approach achieves data efficiency and exploits sparse matrix algebra. Experiments on three multi-relational datasets are conducted to validate the logistic additive approach.