Supervised Bayesian Tensor Factorization for Multi-relational Data in Product Design

Multi-relational data (e.g., product design knowledge graph) learning has attracted great attention in the various research areas recently. Motivated by the assumption that the entities have appropriate relations with respect to their belonging categories (e.g., two entities in the {process parameter} and the {machine unit} categories respectively likely bear upon the relationship of {belong to}), this paper proposes a tensor factorization approach for multi-relational data in a supervised way from a Bayesian perspective, referred as supervised Bayesian tensor factorization (SetF). The proposed SetF is formulated as a joint optimization framework of probabilistic inference and e-insensitive support vector regression. The generative-discriminative nature of the proposed SetF is able to discover the latent representation of multi-relational data under the principle of the max-margin learning. The interplay between entities, relations, and the categories (entities involved) yields more predictive latent representations that are particularly appropriate for discriminant analysis. We conduct a set of experimental comparisons in terms of multi-relational learning tasks over several benchmark datasets, and also over antenna arrays design parameter relation analysis, which demonstrate the effectiveness of the proposed method.