Graph Embeddings
Multi-partition Embedding Interaction
Introduced by Tran et al. in Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph CompletionMEI introduces the multi-partition embedding interaction technique with block term tensor format to systematically address the efficiency--expressiveness trade-off in knowledge graph embedding. It divides the embedding vector into multiple partitions and learns the local interaction patterns from data instead of using fixed special patterns as in ComplEx or SimplE models. This enables MEI to achieve optimal efficiency--expressiveness trade-off, not just being fully expressive. Previous methods such as TuckER, RESCAL, DistMult, ComplEx, and SimplE are suboptimal restricted special cases of MEI.
Source: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Graph Embedding | 4 | 14.81% |
| Knowledge Graph Embedding | 4 | 14.81% |
| Link Prediction | 4 | 14.81% |
| Knowledge Graphs | 3 | 11.11% |
| Question Answering | 3 | 11.11% |
| Recommendation Systems | 3 | 11.11% |
| Knowledge Graph Completion | 2 | 7.41% |
| Object Detection | 1 | 3.70% |
| Computational Efficiency | 1 | 3.70% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
