Link Prediction on N-ary Relational Facts: A Graph-based Approach
Quan Wang, Haifeng Wang, Yajuan Lyu, Yong Tian Zhu · 2021
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 nary 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.1