Link Prediction on N-ary Relational Facts: A Graph-based Approach
  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.
