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Complex Query Answering On Fb15K 237
Metrics
MRR 1p
MRR 2i
MRR 2p
MRR 2u
MRR 3i
MRR 3p
MRR ip
MRR pi
MRR up
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| QTO | 0.490 | 0.431 | 0.214 | 0.227 | 0.568 | 0.212 | 0.280 | 0.381 | 0.214 | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization | |
| GNN-QE | 0.428 | 0.383 | 0.147 | 0.162 | 0.541 | 0.118 | 0.189 | 0.311 | 0.134 | Neural-Symbolic Models for Logical Queries on Knowledge Graphs | |
| CQD | - | - | - | - | 0.486 | - | - | - | - | Complex Query Answering with Neural Link Predictors | |
| CQDA | 0.467 | 0.345 | 0.136 | 0.176 | 0.483 | 0.114 | 0.209 | 0.274 | 0.114 | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering | - |
| BetaE | 0.39 | 0.288 | 0.109 | 0.124 | 0.425 | 0.1 | 0.126 | 0.224 | 0.097 | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs | |
| Q2B | 0.406 | 0.295 | 0.094 | 0.113 | 0.423 | 0.068 | 0.126 | 0.212 | 0.076 | Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings | |
| GQE | 0.35 | 0.233 | 0.072 | 0.082 | 0.346 | 0.053 | 0.107 | 0.165 | 0.057 | Embedding Logical Queries on Knowledge Graphs | |
| CQD-CO | - | - | - | - | - | - | - | - | - | Complex Query Answering with Neural Link Predictors | |
| CQD-Beam | - | - | - | - | - | - | - | - | - | Complex Query Answering with Neural Link Predictors |
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