Out-of-Sample Representation Learning for Multi-Relational Graphs
This addresses a surprisingly under-explored problem in knowledge graph reasoning for non-attributed graphs, though it appears incremental in scope.
The paper tackles the problem of out-of-sample representation learning for non-attributed knowledge graphs, where predictions are needed for entities unseen during training, and creates benchmark datasets while developing and comparing several models and baselines.
Many important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs(where each entity has an initial feature vector) and non-attributed graphs (where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph. However, this problem is surprisingly under-explored for non-attributed graphs. In this paper, we study 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.