Complex Query Answering On Fb15K
المقاييس
MRR 1p
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MRR 3p
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النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
| Paper Title | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| QTO | 0.895 | 0.803 | 0.674 | 0.767 | 0.836 | 0.588 | 0.740 | 0.752 | 0.613 | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization |
| CQDA | 0.892 | 0.761 | 0.645 | 0.684 | 0.794 | 0.579 | 0.706 | 0.701 | 0.579 | Adapting Neural Link Predictors for Data-Efficient Complex Query Answering |
| CQD | 0.892 | 0.771 | 0.653 | 0.723 | 0.806 | - | 0.716 | - | - | Complex Query Answering with Neural Link Predictors |
| GNN-QE | 0.885 | 0.797 | 0.693 | 0.741 | 0.835 | 0.587 | 0.704 | 0.699 | 0.610 | Neural-Symbolic Models for Logical Queries on Knowledge Graphs |
| Q2B | 0.68 | 0.551 | 0.21 | 0.351 | 0.665 | 0.142 | 0.261 | 0.394 | 0.167 | Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings |
| BetaE | 0.651 | 0.558 | 0.257 | 0.401 | 0.665 | 0.247 | 0.281 | 0.439 | 0.252 | Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs |
| GQE | 0.546 | 0.397 | 0.153 | 0.221 | 0.514 | 0.108 | 0.191 | 0.276 | 0.116 | Embedding Logical Queries on Knowledge Graphs |
| CQD-CO | - | - | - | - | - | - | - | - | - | Complex Query Answering with Neural Link Predictors |
| CQD-Beam | - | - | - | - | - | - | - | - | - | Complex Query Answering with Neural Link Predictors |
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