RelDiff 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 ClassificationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Entity Embeddings | 1 | 25.00% |
Knowledge Graph Embeddings | 1 | 25.00% |
Sensitivity Classification | 1 | 25.00% |
Text Classification | 1 | 25.00% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |