Research map / Graph learning / Knowledge and temporal graphs
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Beta distribution embeddings (BetaE) vs boxes (q2b) | KG query embedding · FB15k EPFO | MRR: 38.0 -> 41.6 | Helped |
| BetaE vs q2b | KG query embedding · FB15k-237 EPFO | MRR: 20.1 -> 20.9 | Helped |
| BetaE vs q2b | KG query embedding · NELL995 EPFO | MRR: 22.9 -> 24.6 | Helped |
| BetaE vs q2b | KG query embedding · FB15k 1p | MRR: 68.0 -> 65.1 | Hurt |
| BetaE vs q2b | KG query embedding · NELL995 2p | MRR: 14.0 -> 13.0 | Hurt |
| model disjunction via De Morgan instead of DNF (ablation) | BetaE · FB15k 2u | MRR: 40.1 -> 25.0 | Hurt |
| model disjunction via De Morgan instead of DNF (ablation) | BetaE · NELL995 up | MRR: 8.5 -> 8.6 | No clear effect |
| Beta differential entropy vs box size as uncertainty | query embedding · FB15k-237 2p | Spearman correlation: 0.226 -> 0.503 | Helped |
Closest papers by meaning
- Neural Methods for Logical Reasoning over Knowledge Graphs (ICLR 2022 · similarity 0.94)
- ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs (NeurIPS 2021 · similarity 0.90)
- Faithful Embeddings for Knowledge Base Queries (NeurIPS 2020 · similarity 0.90)
- An Evaluation of Approaches to Train Embeddings for Logical Inference (Student Abstract) (AAAI 2025 · similarity 0.88)
- Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors (ICLR 2024 · similarity 0.88)
- Improving Soft Unification with Knowledge Graph Embedding Methods (ICML 2025 · similarity 0.88)
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