Graph Embeddings
RelDiff
Introduced by Narvala et al. in RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity ClassificationRelDiff generates entity-relation-entity embeddings in a single embedding space. RelDiff adopts two fundamental vector algebraic operators to transform entity and relation embeddings from knowledge graphs into entity-relation-entity embeddings. In particular, RelDiff can encode finer-grained information about the relations than is captured when separate embeddings are learned for the entities and the relations.
Source: RelDiff: Enriching Knowledge Graph Relation Representations for Sensitivity Classification
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Entity Embeddings | 1 | 25.00% |
| Knowledge Graph Embeddings | 1 | 25.00% |
| Sensitivity Classification | 1 | 25.00% |
| Text Classification | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
