Complex Query Answering On Fb15K 237
평가 지표
MRR 1p
MRR 2i
MRR 2p
MRR 2u
MRR 3i
MRR 3p
MRR ip
MRR pi
MRR up
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | 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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