Papers with recovering missing facts
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder (P18-1)
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| Challenge: | Embedding models for entities and relations are useful for recovering missing facts in knowledge bases. |
| Approach: | They propose a dimension reduction technique by training relations jointly with an autoencoder to capture compositional constraints. |
| Outcome: | The proposed model improves on Knowledge Base Completion tasks with a significantly higher mean rank and better compositional training. |