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A collection of Tensorflow implementations of embeddings for entities.

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TensorFlow-EFE

A collection of Tensorflow implementations of embeddings for entities.

Requirements

  • Python 3
  • Tensorflow >= 1.2
  • Hyperopt

Models

The generic abstract model is defined in model.py. All specific models are implemented in efe.py

Model Implementations Reference
TransE TransE_L2; TransE_L1 Bordes et al. (NIPS 2013)
NTN NTN Socher et al. (NIPS 2013)
DistMult DistMult; DistMult_tanh Yang et al. (ICLR 2015)
ComplEx Complex; Complex_tanh Trouillon et al. (ICML 2016)

Preprocess

python preprocess.py -d [data_name]

Hyperparameters

Set hyperparameters

Add hyperparameters dict and its identifier in model_param_space.py.

Search optimal hyperparameters

python task.py -m [model_name] -d [data_name] -e [max_evals] -c [cv_runs]

model_name is the identifier defined in the model_param_space.py. data_name is either wn18 or fb15k. max_evals is the maximum runs to search the hyperparameters, default: 100. cv_runs is the number of runs for the cross validation, default: 3.

The search process and result are stored in log folder.

Evaluation

python train.py -m [model_name] -d [data_name]

Train on the given hyperparameter setting and give the result for the test set.

Performance

Model WN18 FB15K
Filtered MRR Hits@1 Hits@3 Hits@10 Filtered MRR Hits@1 Hits@3 Hits@10
TransE 0.454 0.089 0.814 0.954 0.407 0.272 0.480 0.657
DistMult 0.868 0.786 0.948 0.970 0.761 0.691 0.815 0.875
ComplEx 0.971 0.969 0.973 0.974 0.768 0.676 0.843 0.908

License

MIT

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A collection of Tensorflow implementations of embeddings for entities.

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