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Deep Learning-based Ensemble Method for Temporal Knowledge Graph Embedding in Link Prediction.

Three experiments are conducted in this project.

  • Each individual TKGE model is re-predicted based on the same dataset as other experiments, namely DE-SimplE, DE-DistMult, DE-TransE, ATiSE, and TERO.
  • To demonstrate that the neural network can learn better weights than other methods, conducted the grid search experiment as the baseline method.
  • Conducted two neural network experiments with different numbers of inputs. For 5 inputs, it took the simulated score of the top 1 prediction for each TKGE model, and for 25 inputs, it took the simulated score of the top 5 predictions for each TKGE model.

TKGE Models

  • DE-SimplE (including DE-SimplE, DE-DistMult, and DE-TransE)
  • TERO
  • ATiSE

TKGE Dataset

  • ICEWS14

Neural Network Models

  • 25input
  • 5input

Neural Network Dataset

  • 25t_5w has 25 inputs corresponding to the top 5 predictions of each TKGE model, and target values are the rank of the correct answer of each TKGE model.
  • 5p_5w has 5 inputs corresponding to 5 TKGE models, 5 predictions, and target values are the rank of the correct answer of each TKGE model.