Papers by Aleksandar Pavlović
SpeedE: Euclidean Geometric Knowledge Graph Embedding Strikes Back (2024.findings-naacl)
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| Challenge: | Geometric knowledge graph embedding models (gKGEs) have shown great potential for knowledge graph completion (KGC) however, contemporary gKges require high embeddable dimensionalities or complex embeddances for good KGC performance, drastically limiting their time and space efficiency. |
| Approach: | They propose a lightweight Euclidean gKGE that provides strong inference capabilities and significantly outperforms state-of-the-art gGKGEs. |
| Outcome: | The proposed model outperforms state-of-the-art gKGEs on YAGO3-10 and WN18RR while significantly increasing their efficiency. |