论文标题
超关系知识图的学习表示
Learning Representations for Hyper-Relational Knowledge Graphs
论文作者
论文摘要
知识图（kgs）因其学习单一关系事实的表示能力而获得了突出。最近，研究重点是建模超级关系事实，这些事实超出了单一关系事实的限制，使我们能够代表更复杂和现实的信息。但是，现有的超级关系中学习表征的方法主要集中于增强从预选赛到基础三元组的沟通，同时忽略了从基本三重限度到资格赛的信息流。这可能会导致次优的预选赛表示，尤其是在提出大量预选赛时。它促使我们设计一个利用多个聚合器来学习超级关系事实的表示框架：一个从基本三重的角度来看，另一个从预选赛的角度来看。实验证明了我们在多个数据集中完成超相关知识图完成框架的有效性。此外，我们进行了一项消融研究，以验证各个组成部分在我们的框架中的重要性。可以在\ url {https://github.com/harryshomer/quad}找到重现我们结果的代码。
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of uni-relational facts and allow us to represent more complex and real-world information. However, existing approaches for learning representations on hyper-relational KGs majorly focus on enhancing the communication from qualifiers to base triples while overlooking the flow of information from base triple to qualifiers. This can lead to suboptimal qualifier representations, especially when a large amount of qualifiers are presented. It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers. Experiments demonstrate the effectiveness of our framework for hyper-relational knowledge graph completion across multiple datasets. Furthermore, we conduct an ablation study that validates the importance of the various components in our framework. The code to reproduce our results can be found at \url{https://github.com/HarryShomer/QUAD}.
