Aleksandar Pavlović

2 accepted papers

2024

SpeedE: Euclidean Geometric Knowledge Graph Embedding Strikes Back

NAACL 2024findings

Geometric knowledge graph embedding models (gKGEs) have shown great potential for knowledge graph completion (KGC), i.e., automatically predicting missing triples. However, contemporary gKGEs require high embedding dimensionalities or complex embedding spaces for good KGC performance, drastically li…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2023

ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion

Aleksandar Pavlović, Emanuel Sallinger

ICLR 2023top-25%

Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet…

Cited by 40 Source ↗PDF ↗code ↗Scholar ↗