Gaohao Wu

ACKGE: knowledge graph embedding with probabilistic semantic bounds for adaptive contrastive learning

The Adaptive Contrastive Knowledge Graph Embedding (ACKGE) framework, which uses the empirical mean and standard deviation of entity similarities to define probabilistic semantic bounds, achieves competitive performance against strong baselines in link prediction tasks, particularly in handling complex relational structures.