Graph Embeddings
RotatE
Introduced by Sun et al. in RotatE: Knowledge Graph Embedding by Relational Rotation in Complex SpaceRotatE is a method for generating graph embeddings which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a self-adversarial negative sampling technique.
Source: RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Graph Embedding | 19 | 17.12% |
| Knowledge Graph Embedding | 18 | 16.22% |
| Knowledge Graphs | 13 | 11.71% |
| Link Prediction | 13 | 11.71% |
| Knowledge Graph Completion | 11 | 9.91% |
| Entity Embeddings | 4 | 3.60% |
| Knowledge Graph Embeddings | 3 | 2.70% |
| Translation | 3 | 2.70% |
| Decoder | 2 | 1.80% |
Self-Adversarial Negative Sampling
