Research map / Graph learning / Knowledge and temporal graphs
Knowledge Graph Completion with Adaptive Sparse Transfer Matrix
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace dense transfer matrices with adaptive sparse ones (TranSparse share) | TransR · FB15k link prediction | Hits@10 (filter): 68.7 -> 78.3 | Helped |
| use separate head and tail sparse matrices (ablation) | TranSparse(share) · FB15k link prediction | Hits@10 (filter): 78.3 -> 79.9 | Helped |
| use separate head and tail sparse matrices (ablation) | TranSparse(share) · WN18 link prediction | Hits@10 (filter): 93.9 -> 93.2 | Hurt |
| use unstructured instead of structured sparse pattern (ablation) | TranSparse(separate) · FB15k link prediction | Hits@10 (filter): 79.5 -> 79.9 | Helped |
| TranSparse vs TransD | knowledge graph embedding · FB13 triplet classification | accuracy: 89.1 -> 88.2 | Hurt |
| TranSparse vs TransD | knowledge graph embedding · WN11 triplet classification | accuracy: 86.4 -> 86.8 | Helped |
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