Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction

TL;DR

This paper introduces DEKG-ILP, a novel model for inductive link prediction in disconnected emerging knowledge graphs, effectively predicting both enclosing and bridging links by combining global semantic features and local subgraph topology.

Contribution

The paper proposes DEKG-ILP, which uniquely addresses both enclosing and bridging link prediction in disconnected emerging KGs using contrastive learning and GNN-based subgraph modeling.

Findings

Significant performance improvements over state-of-the-art methods.

Effective extraction of global relation-based features.

Accurate local subgraph topological information modeling.

Abstract

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction. The source code is available online.