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Complex Query Answering On Nell 995
评估指标
MRR 1p
MRR 2i
MRR 2p
MRR 2u
MRR 3i
MRR 3p
MRR ip
MRR pi
MRR up
评测结果
各个模型在此基准测试上的表现结果
| 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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