knowledge-graph-completion

Proposes RelBall, a KGC model that extends Rotate3D with modulus transformation for modeling hierarchies and a tail-centric relation ball to handle one-to-many relations, achieving competitive link prediction performance.

This paper introduces CORE, a new knowledge graph completion model that uses cyclic orthotope relation embeddings on a torus manifold to address boundary constraints in region-based models. Experiments show competitive performance in link prediction tasks.

This paper proposes M-Hyper, a novel multi-modal knowledge graph completion method that balances fusion and independence of modality representations using hypercomplex (biquaternion) algebra. The approach introduces Fine-grained Entity Representation Factorization and Robust Relation-aware Modality Fusion modules to achieve state-of-the-art performance with improved robustness.