Complex Query Answering On Nell 995
Metriken
MRR 1p
MRR 2i
MRR 2p
MRR 2u
MRR 3i
MRR 3p
MRR ip
MRR pi
MRR up
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | MRR 1p | MRR 2i | MRR 2p | MRR 2u | MRR 3i | MRR 3p | MRR ip | MRR pi | MRR up | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|---|---|
| CQDA | 0.604 | 0.434 | 0.229 | 0.200 | 0.526 | 0.167 | 0.264 | 0.321 | 0.170 | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering | - |
| QTO | 0.607 | 0.425 | 0.241 | 0.204 | 0.506 | 0.216 | 0.265 | 0.313 | 0.179 | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization | |
| CQD | 0.604 | 0.436 | - | - | - | - | 0.256 | - | - | Complex Query Answering with Neural Link Predictors | |
| Q2B | 0.422 | 0.333 | 0.140 | 0.113 | 0.445 | 0.112 | 0.168 | 0.224 | 0.1103 | Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings | |
| GNN-QE | 0.533 | 0.424 | 0.189 | 0.159 | 0.525 | 0.149 | 0.189 | 0.308 | 0.126 | Neural-Symbolic Models for Logical Queries on Knowledge Graphs | |
| BetaE | 0.53 | 0.376 | 0.13 | 0.122 | 0.475 | 0.114 | 0.143 | 0.241 | 0.085 | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs |
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