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Complex Query Answering On Nell 995
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 | 0.607 | 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 | |
| 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 | - |
| CQD | 0.604 | 0.436 | - | - | - | - | 0.256 | - | - | Complex Query Answering with Neural Link Predictors | |
| 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 | |
| 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 |
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