Link Prediction On Fb15K

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이 벤치마크에서 각 모델의 성능 결과

			Paper Title
DistMult (after variational EM)	0.914	0.841	Augmenting and Tuning Knowledge Graph Embeddings
AutoKGE	0.914	0.861	AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
HHolE	.901	.796	Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations
QuatE	0.900	0.833	Quaternion Knowledge Graph Embeddings
OPTransE	0.899	-	Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
MEI-BTD	0.893	0.806	Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
TuckER	0.892	0.795	TuckER: Tensor Factorization for Knowledge Graph Completion
ConvR	0.887	0.782	Adaptive Convolution for Multi-Relational Learning
SEEK	0.886	0.825	SEEK: Segmented Embedding of Knowledge Graphs
HypER	0.885	0.790	Hypernetwork Knowledge Graph Embeddings
pRotatE	0.884	0.799	RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
RotatE	0.884	0.797	RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
JoBi ComplEx	0.883	0.761	Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
SimplE	0.876	0.779	GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
LogicENN	0.874	0.766	LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
Relational-GCN	0.842	0.696	-
Complex	0.840	0.692	Knowledge Graph Completion via Complex Tensor Factorization
SimplE	0.838	0.727	SimplE Embedding for Link Prediction in Knowledge Graphs
ConvE	0.831	0.657	Convolutional 2D Knowledge Graph Embeddings
TransD	0.773	-	-

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Link Prediction On Fb15K

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