Evaluation of graph self-supervised learning (GSSL) methods — feature reconstruction, relation reconstruction, and contrastive learning — on text-derived knowledge graphs for ontology learning tasks, using multiple GNN backbones with different message-passing mechanisms.
Analysis of the impact of structural and semantic noise — fragmentation, sparsity, incorrect triplets, duplicated entities — in automatically constructed text-derived knowledge graphs on downstream representation learning and entity typing performance.