Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

翻译：链接预测是完成知识图表(KGs)的重要方法,而基于嵌入方法(KGs的连接预测有效)在关系上表现不佳,只有几个关联三重关系。在这项工作中,我们提出一个元关系学习框架(MetaR)来完成KGs中共同但具有挑战性的几发链接预测,即通过只观察几个关联三重来预测新的三重关系。我们通过侧重于传输特定关联元信息,使模型学习最重要的知识和学习更快,分别与MetaR的元元和梯元相对应。 简而言之,我们的模型在几发链接预测KG基准上取得了最新的结果。