Before revision:  In this paper, we introduce the out-of-sample representation learning problem for non-attributed multi-relational graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.
After revision:  In this paper, we introduce the out-of-sample representation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.