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Link Property Prediction On Ogbl Wikikg2

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Validation MRR

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이 벤치마크에서 각 모델의 성능 결과

Paper Title
RelEnsNo21767676220.7392 ± 0.00110.7509 ± 0.0009Relation-aware Ensemble Learning for Knowledge Graph Embedding
StarGraph + TripleRE + TextYes19273953300.7305 ± 0.00100.7442 ± 0.0006-
ComplEx (250dim)No12505695000.4027 ± 0.00270.3759 ± 0.0016Complex Embeddings for Simple Link Prediction
DistMult (500dim)No12505695000.3729 ± 0.00450.3506 ± 0.0042Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TransE (500dim)No12505695000.4256 ± 0.00300.4272 ± 0.0030-
RotatE (250dim)No12504357500.4332 ± 0.00250.4353 ± 0.0028RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Rot-ProNo10006696020.5602 ± 0.00160.5740 ± 0.0008Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
RotProNo10006696020.4277 ± 0.00080.4174 ± 0.0058Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
CompoundE3DNo7506627000.7006 ± 0.00110.7175 ± 0.0015Knowledge Graph Embedding with 3D Compound Geometric Transformations
tripleRENo5007633370.5794 ± 0.00200.6045 ± 0.0024-
TransERRNo5004418020.6359 ± 0.00200.6518 ± 0.0012TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
PairRE (200dim)No5003348000.5208 ± 0.00270.5423 ± 0.0020PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
ComplEx-N3-RP (100dim)-5003348000.64810.6701Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
AutoSFNo5002278000.5458 ± 0.00520.5510 ± 0.0063AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
ComplEx-N3-RP (50dim)-2501674000.63640.6594Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
ComplEx-RP (50dim)No2501674000.6392 ± 0.00450.6561 ± 0.0070-
ComplEx (50dim)No2501139000.3804 ± 0.00220.3534 ± 0.0052Complex Embeddings for Simple Link Prediction
DistMult (100dim)No2501139000.3447 ± 0.00820.3150 ± 0.0088Embedding Entities and Relations for Learning and Inference in Knowledge Bases
TransE (100dim)No2501139000.2622 ± 0.00450.2465 ± 0.0020-
RotatE (50dim)No2500871500.2530 ± 0.00340.2250 ± 0.0035RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
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