论文标题
多关系图的样本外表示学习
Out-of-Sample Representation Learning for Multi-Relational Graphs
论文作者
论文摘要
在知识图中，可以将许多重要问题作为推理提出。表示学习对跨置推理非常有效，其中人们需要对已经观察到的实体做出新的预测。对于两个属性图（每个实体都有初始特征向量）和非属性图（其中唯一的初始信息来自已知与其他实体的关系），这是正确的。对于样本外推理，需要对训练时间看不见的实体进行预测，因此，许多先前的工作都认为归因于图。但是，对于非属性图，此问题令人惊讶地探索了。在本文中，我们研究了非属性知识图的样本外表示问题，为此任务创建基准数据集，开发多种模型和基准，并提供所提出模型和基准的经验分析和比较。
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.
