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Knowledge Graph Completion on WN18 (test)

0.987Hits@10LERP
0.095720.327110.55850.78989May 6, 2017Aug 10, 2018Nov 15, 2019Feb 19, 2021May 26, 2022Aug 31, 2023Dec 5, 2024

Evaluation results

MethodLinks
LERPCategory=Rule Learning2023.050.987—————0.95893.298.2—
Best PublishedSetting=Past SOTA2018.060.97—————0.94———
DaBRScoring Function=Semantic and Geometric (SG)2024.120.966—————0.95494.695.956
TransERRScoring Function=Geometric Distance (GD)2024.120.965—————0.95394.595.782
Inverse ModelCategory=No Rule Learning2023.050.964—————0.96395.396.4—
QuatREScoring Function=Semantic Matching (SM)2024.120.963—————0.93994.695.3116
RotateCTScoring Function=Geometric Distance (GD)2024.120.963—————0.95194.495.6201
Rotate4DScoring Function=Geometric Distance (GD)2024.120.963—————0.95294.695.6173
DCNEScoring Function=Geometric Distance (GD)2024.120.963—————0.95294.595.5192
DualEScoring Function=Semantic Matching (SM)2024.120.962—————0.95294.695.6156
AutoBLMModel Category=AutoBLM2021.070.961—————0.95294.7——
AutoBLM+Model Category=AutoBLM2021.070.961—————0.95294.7——
LinearRECategory=No Rule Learning2023.050.961—————0.95294.795.5—
QuatDECategory=No Rule Learning2023.050.961—————0.9594.495.4—
Rotate3DScoring Function=Geometric Distance (GD)2024.120.961—————0.95194.595.3214
ComplEx-N3Setting=Standard, Regularizer=N32018.060.96—————0.95———
CP-N3Setting=Reciprocal, Regularizer=N32018.060.96—————0.95———
ComplEx-FROSetting=Reciprocal, Regularizer=FRO2018.060.96—————0.95———
ComplEx-N3Setting=Reciprocal, Regularizer=N32018.060.96—————0.95———
Trans4EScoring Function=Geometric Distance (GD)2024.120.96—————0.9594.495.3175
RotatEModel Category=TDM2021.070.959—————0.94994.4——
SimplE/CPModel Category=BLM2021.070.959—————0.9594.5——
QuatEModel Category=BLM2021.070.959—————0.9594.5——
QuatEScoring Function=Semantic Matching (SM)2024.120.959—————0.9594.595.4162
TuckERModel Category=BLM2021.070.958—————0.95394.9——
TuckERScoring Function=Semantic Matching (SM)2024.120.958—————0.95394.995.5—
HolE/ComplExModel Category=BLM2021.070.957—————0.95194.5——
AnalogyModel Category=BLM2021.070.957—————0.9594.6——
Quaternion-based four-embeddingModel type=Interaction mechanism2019.030.956———0.95—0.94193.1——
ConvECategory=No Rule Learning2023.050.956—————0.94393.594.6—
ConvEModel Category=NNM2021.070.955—————0.94293.5——
DRUMCategory=Rule Learning2023.050.954—————0.94493.994.3—
RSNModel Category=NNM2021.070.953—————0.9492.2——
DistMultModel Category=BLM2021.070.952—————0.82171.7——
ComplExWeight setting=(1, 0, 0, 1, 0, -1, 1, 0)2019.030.951———0.946—0.93792.8——
ComplExSetting=Past SOTA2018.060.95—————0.94———
ConvESetting=Past SOTA2018.060.95—————0.94———
CP-FROSetting=Reciprocal, Regularizer=FRO2018.060.95—————0.95———
Good example 2Weight setting=(1, 1, -1, 1, 1, -1, 1, 1)2019.030.95———0.944—0.93893——
HolEEvaluation Protocol=filtered, Modeling Analogies=true, External Data Usage=false2017.050.949—————————
CPhWeight setting=(0, 0, 1, 0, 0, 1, 0, 0)2019.030.949———0.944—0.93792.9——
ComplExEvaluation Protocol=filtered, Modeling Analogies=true, External Data Usage=false2017.050.947—————————
ANALOGYEvaluation Protocol=filtered, Modeling Analogies=true, External Data Usage=false2017.050.947—————————
Bad example 2Weight setting=(0, 0, 1, 1, 1, 1, 0, 0)2019.030.947———0.917—0.79466.6——
ComplExCategory=No Rule Learning2023.050.947—————0.94193.693.6—
Good example 1Weight setting=(0, 0, 20, 1, 1, 20, 0, 0)2019.030.946———0.942—0.93893.4——
Auto weightRestriction=sigmoid, Range=(0, 1)2019.030.946———0.915—0.78966.1——
TransHModel Category=TDM2021.070.945—————0.521———
DistMultWeight setting=(1, 0, 0, 0, 0, 0, 0, 0)2019.030.945———0.915—0.79667.4——
Auto weightRestriction=sigmoid, Range=(0, 1), Sparsity=sparse2019.030.945———0.915—0.79366.7——
Neural LPCategory=Rule Learning2023.050.945—————0.94———
Uniform weightWeight setting=(1, 1, 1, 1, 1, 1, 1, 1)2019.030.944———0.915—0.78765.8——
Auto weightRestriction=none2019.030.944———0.911—0.77463.6——
Auto weightRestriction=softmax, Range=(0, 1)2019.030.944———0.915—0.80268.5——
Auto weightRestriction=softmax, Range=(0, 1), Sparsity=sparse2019.030.944———0.915—0.80368.8——
NLFEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=true2017.050.943—————————
Auto weightRestriction=tanh, Range=(-1, 1)2019.030.943———0.908—0.76562.5——
Auto weightRestriction=tanh, Range=(-1, 1), Sparsity=sparse2019.030.943———0.91—0.76361.3——
TransECategory=No Rule Learning2023.050.943—————0.49511.388.8—
DistMultSetting=Past SOTA2018.060.94—————0.82———
TransSparseEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.939—————————
DistMultEvaluation Protocol=filtered, Modeling Analogies=true, External Data Usage=false2017.050.936—————————
Auto weightRestriction=none, Sparsity=sparse2019.030.935———0.892—0.79268.5——
STransEEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.934—————————
KG2EEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.932—————————
CTransREvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.923—————————
TransDEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.922—————————
TransREvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.92—————————
TransEEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.892—————————
TransHEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.867—————————
LFMEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.816—————————
SEEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.805—————————
SMEEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.741—————————
NTNEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.661—————————
RESCALEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.528—————————
UnstructuredEvaluation Protocol=filtered, Modeling Analogies=false, External Data Usage=false2017.050.382—————————
CP-N3Setting=Standard, Regularizer=N32018.060.33—————0.2———
Bad example 1Weight setting=(0, 0, 20, 0, 0, 1, 0, 0)2019.030.159———0.116—0.1077.9——
CPWeight setting=(0, 0, 1, 0, 0, 0, 0, 0)2019.030.139———0.093—0.0865.9——
CPSetting=Past SOTA2018.060.13—————0.08———
ComplExrank (K)=150, negative samples (η)=12017.02—0.9410.5870.9360.9450.947————
CP2017.02—0.0750.0580.0490.080.125————
DistMult2017.02—0.8220.5320.7280.9140.936————
HolEresults source=Nickel et al. (2016b)2017.02—0.9380.6160.930.9450.949————
RESCAL2017.02—0.8940.5830.8670.9180.935————
TransE2017.02—0.4540.3350.0890.8230.934————