Link prediction on knowledge graphs (KGs) is a key research topic. Previous work mainly focused on binary relations, paying less attention to higher-arity relations although they are ubiquitous in real-world KGs. This paper considers link prediction upon n-ary relational facts and proposes a graph-based approach to this task. The key to our approach is to represent the n-ary structure of a fact as a small heterogeneous graph, and model this graph with edge-biased fully-connected attention. The fully-connected attention captures universal inter-vertex interactions, while with edge-aware attentive biases to particularly encode the graph structure and its heterogeneity. In this fashion, our approach fully models global and local dependencies in each n-ary fact, and hence can more effectively capture associations therein. Extensive evaluation verifies the effectiveness and superiority of our approach. It performs substantially and consistently better than current state-of-the-art across a variety of n-ary relational benchmarks. Our code is publicly available.

翻译：知识图形(KGs)的链接预测是一个关键的研究课题。以前的工作主要侧重于二进制关系,较少关注更高性关系,尽管在现实世界KGs中,它们无处不在。本文考虑将N-ary关系事实的预测联系起来,并提议了一种基于图表的方法来完成这项任务。我们的方法的关键是将事实的n-ary结构作为一个小的多元图,并以边缘偏差的完全相连的注意作为该图的模型。完全相连的注意力捕捉了普遍的纵向相互作用,同时对特别编码图形结构及其异质的偏差给予了深视。这样,我们的方法充分模拟了每个n-ary事实的全球和地方依赖关系,从而可以更有效地捕捉其中的关联。广泛的评价可以验证我们方法的有效性和优势。它比目前各种n-ary关系基准的先进程度要高得多。我们的代码是公开的。