Link Prediction On Fb15K 1
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| Paper Title | ||||||
|---|---|---|---|---|---|---|
| ComplEx-N3 (reciprocal) | - | 0.91 | - | - | 0.86 | Canonical Tensor Decomposition for Knowledge Base Completion |
| LineaRE | 0.805 | 0.906 | 0.867 | - | 0.843 | LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction |
| HHolE | 0.727 | 0.901 | 0.848 | - | 0.796 | Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations |
| MEI (small) | 0.757 | 0.878 | 0.823 | - | 0.800 | Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion |
| PyTorch BigGraph (ComplEx) | - | 0.872 | - | - | 0.79 | PyTorch-BigGraph: A Large-scale Graph Embedding System |
| ComplEx NSCaching | - | 0.8682 | - | - | 0.7721 | NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding |
| Rule-Guided Embedding | 0.703 | 0.865 | 0.815 | 0.836 | 0.768 | Knowledge Graph Embedding with Iterative Guidance from Soft Rules |
| MDE | - | 0.857 | - | - | 0.652 | MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs |
| HolE | 0.402 | 0.739 | 0.613 | - | 0.524 | Holographic Embeddings of Knowledge Graphs |
| ParTransH | - | 0.468 | - | - | - | Efficient Parallel Translating Embedding For Knowledge Graphs |
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