Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings

The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs. Current state-of-the-art classification models rely on learning per-entity latent representations by mining the whole structure of the relations' graph, however, they still face two major problems. Firstly, it is very challenging to generate expressive latent representations in sparse multi-relational settings with implicit feedback relations as there is very little information per-entity. Secondly, for entities with structured properties such as titles and abstracts (text) in documents, models have to be modified ad-hoc. In this paper, we aim to overcome these two main drawbacks by proposing a flexible nonlinear latent embedding model (BRNLE) for the classification of multi-relational data. The proposed model can be applied to entities with structured properties such as text by utilizing the numerical vector representations of those properties. To address the sparsity problem of implicit feedback relations, the model is optimized via a sparsely-regularized multi-relational pair-wise Bayesian personalized ranking loss (BPR). Experiments on four different real-world datasets show that the proposed model significantly outperforms state-of-the-art models for multi-relational classification.