Integrating global semantics and enhanced local subgraph for inductive link prediction

Inductive link prediction (ILP) predicts missing triplets involving unseen entities in knowledge graphs (KGs). Existing ILP research mainly addresses seen-unseen entities in the original KG (semi-inductive link prediction) and unseen-unseen entities in emerging KGs (fully-inductive link prediction). Bridging-inductive link prediction, which focuses on unseen entities that carry evolutionary information from the original KG to the emerging KG, has not been extensively studied so far. This study introduces a novel model called GSELI (integrating global semantics and enhanced local subgraph for inductive link prediction), which comprises three components. (1) The contrastive learning-based global semantic features (CLSF) module extracts relation-specific semantic features between the original and emerging KGs and employs semantic-aware contrastive learning to optimize these features. (2) The GNN-based enhanced local subgraph (GELS) module employs personalized PageRank (PPR)-based local clustering to sample tightly-related subgraphs and incorporates complete neighboring relations to enhance the topological information of subgraphs. (3) Joint contrastive learning and supervised learning training. Experimental results on various benchmark datasets demonstrate that GSELI outperforms the baseline models in both fully-inductive and bridging-inductive link predictions.