  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.
