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Link Prediction on WikiKG90M v2 (val)

0.301MRRKGT5-context, T5 base
0.102360.153930.20550.25707Mar 19, 2022May 29, 2022Aug 9, 2022Oct 19, 2022Dec 30, 2022Mar 11, 2023May 22, 2023

Evaluation results

MethodLinks
KGT5-context, T5 baseParams=220M2023.050.301—
DGLKE + Rule MiningParams=18.2B2023.050.292—
BESSParams=23.3B2023.050.292—
PIE-RMParams=18.2B2023.050.254—
KGT5Params=60M2022.030.221—
KGT5, T5 smallParams=60M2023.050.221—
TransE-ConcatParams=18.2B2022.030.206—
TransE-ConcatParams=18.2B2023.050.206—
ComplEx-ConcatParams=18.2B2022.030.205—
ComplEx-ConcatParams=18.2B2023.050.205—
ComplExParams=18.2B2023.050.182—
ComplEx-MPNetParams=307K2022.030.126—
ComplExParams=18.2B2022.030.115—
TransE-MPNetParams=307K2022.030.113—
TransEParams=18.2B2022.030.11—
TransEParams=18.2B2023.050.11—
ComplEx2026.03—0.1649
Direct ensemblestrategy=grid search above all models with non-zero weights2026.03—0.28
Ensemble with Model Selectionstrategy=first select best models (TransE-0,TransE-1,TransE-2,ComplEx, OTE-0, OTE-2) then grid search on model weights2026.03—0.2839
NOTE-02026.03—0.1561
NOTE-1negative sample size=12002026.03—0.1648
NOTE-2embeddings=neighbor enhanced embeddings and randomly initialized embeddings w/ MLP layer2026.03—0.1654
NOTE-3negative sample size=1200, embeddings=neighbor enhanced embeddings and randomly initialized embeddings w/ MLP layer2026.03—0.1592
TransE-02026.03—0.214
TransE-1batch size=20480, negative sample size=204802026.03—0.2094
TransE-2batch size=20480, negative sample size=20480, embeddings=MPNet and randomly initialized embeddings w/ MLP layer2026.03—0.2114
TransE-3embeddings=neighbor enhanced embeddings and randomly initialized embeddings w/ MLP layer2026.03—0.1877