5 accepted papers
Knowledge distillation for knowledge graph embedding (KGE) models effectively compresses KGE models by reducing their embedding dimensions. While existing methods distill knowledge from a high-dimensional teacher to a low-dimensional student, they typically rely on a single teacher embedding space,
Taxonomy completion, a self-supervised task, aims to add new concepts to an existing taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers have proposed several approaches to capture the essential relationships in taxonomy using semantic or structural information. However…
Automatic taxonomy completion aims to attach the emerging concept to an appropriate pair of hypernym and hyponym in the existing taxonomy. Existing methods suffer from the overfitting to leaf-only problem caused by imbalanced leaf and non-leaf samples when training the newly initialized classificati…
Taxonomies are hierarchical classifications widely exploited to facilitate downstream natural language processing tasks. The taxonomy expansion task aims to incorporate emergent concepts into the existing taxonomies. Prior works focus on modeling the local substructure of taxonomies but neglect the…
As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a…