Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.
翻译:知识图表(KGs)以其庞大的规模和知识推导能力而著称,但也因与其相关的不完全性而臭名昭著。然而,由于KGs关系的长期分布,提出了几张光的KG完成率,作为缓解不完整程度和扩大KGs覆盖面的一种解决办法。它的目的是在只提供几张培训三胞胎作为参考时,对涉及新关系的新三胞胎作出预测。以前的方法大多侧重于设计当地邻居聚合器,以学习实体一级的信息和/或将连续依赖性假设强加在三胞胎一级,以学习元关系信息。然而,宝贵的三胞三胞胎互动和上下层关系信息被忽略,在学习少发关系的元表示时,基本上被忽略了。在本文中,我们建议了一种等级关系学习方法(HiRe),用于完成几胞三胞胎的KG。通过联合收集三个层次的关系信息(实体一级、三胞级和上下层),HiRe可以有效地学习和改进少发关系的元代表制,并因此对新的无形关系进行非常广泛的超高度试验。