HyperAI
الرئيسية
الأخبار
أحدث الأوراق البحثية
الدروس
مجموعات البيانات
الموسوعة
SOTA
نماذج LLM
لوحة الأداء GPU
الفعاليات
البحث
حول
العربية
HyperAI
Toggle sidebar
البحث في الموقع...
⌘
K
الرئيسية
SOTA
Link Prediction
Link Prediction On Yago3 10
Link Prediction On Yago3 10
المقاييس
Hits@1
Hits@10
Hits@3
MRR
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Hits@1
Hits@10
Hits@3
MRR
Paper Title
Repository
Rot-Pro
0.443
0.699
0.596
0.542
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
HAKE
0.462
0.694
0.596
0.545
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
ComplEx
-
-
-
0.551
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
BoxE
0.494
0.699
-
0.567
BoxE: A Box Embedding Model for Knowledge Base Completion
RefE
0.503
0.712
0.621
0.577
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
CP-DURA (large model)
0.506
0.709
-
0.579
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
-
DensE
0.465
0.678
0.585
0.541
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy
ComplEx-DURA (large model)
0.511
0.713
-
0.584
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion
-
DihEdral
0.381
0.643
0.523
0.472
Relation Embedding with Dihedral Group in Knowledge Graph
-
SAFRAN (white box, rule based)
0.492
0.693
-
0.564
SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models
NBFNet
0.480
0.708
0.612
0.563
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
MEI
0.505
0.709
0.622
0.578
Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
A*Net
0.470
0.707
0.611
0.556
-
-
MEIM
0.514
0.716
0.625
0.585
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
ComplEx-N3 (reciprocal)
-
-
-
0.58
Canonical Tensor Decomposition for Knowledge Base Completion
InteractE
0.462
0.687
-
0.541
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
ConvE
-
0.62
-
0.44
Convolutional 2D Knowledge Graph Embeddings
ComplEx-N3 (large model, reciprocal)
-
0.71
-
-
Canonical Tensor Decomposition for Knowledge Base Completion
0 of 18 row(s) selected.
Previous
Next