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
Model Name | MRR 1p | MRR 2i | MRR 2p | MRR 2u | MRR 3i | MRR 3p | MRR ip | MRR pi | MRR up | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 | |
| 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 | |
| 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 | |
| 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 | - | - | - | - | 0.486 | - | - | - | - | Complex Query Answering with Neural Link Predictors | |
| CQD-CO | - | - | - | - | - | - | - | - | - | Complex Query Answering with Neural Link Predictors | |
| CQD-Beam | - | - | - | - | - | - | - | - | - | 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 | - |
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