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

• URL: http://arxiv.org/abs/2105.08476v1
• Date: Tue, 18 May 2021 12:40:35 GMT
• Title: Link Prediction on N-ary Relational Facts: A Graph-based Approach
• Authors: Quan Wang, Haifeng Wang, Yajuan Lyu, Yong Zhu
• Abstract summary: Link prediction on knowledge graphs (KGs) is a key research topic. This paper considers link prediction upon n-ary relational facts and proposes a graph-based approach to this task.
• Score: 18.01071110085996
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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