Research map / Graph learning / Knowledge and temporal graphs
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| HAKE polar embedding instead of RotatE | KG embedding · YAGO3-10 | MRR: .495 -> .545 | Helped |
| HAKE instead of RotatE | KG embedding · WN18RR | MRR: .476 -> .497 | Helped |
| HAKE instead of RotatE | KG embedding · FB15k-237 | MRR: .338 -> .346 | Helped |
| add phase part to modulus part (ablation) | HAKE · WN18RR | MRR: .240 -> .496 | Helped |
| add modulus part to phase part (pRotatE) (ablation) | HAKE · YAGO3-10 | MRR: .480 -> .522 | Helped |
| add mixture bias (ablation) | HAKE · YAGO3-10 | MRR: .522 -> .545 | Helped |
| add mixture bias (ablation) | HAKE · WN18RR | MRR: .496 -> .497 | No clear effect |
Closest papers by meaning
- You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings (ICLR 2020 · similarity 0.90)
- Relational Pattern Benchmarking on the Knowledge Graph Link Prediction Task (NeurIPS 2021 · similarity 0.90)
- Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (NeurIPS 2021 · similarity 0.90)
- RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space (ICLR 2019 · similarity 0.90)
- Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction (AAAI 2023 · similarity 0.89)
- Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding (NeurIPS 2024 · similarity 0.88)
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