Anonymized Code Repository for "Augmenting and Tuning Knowledge Graph Embeddings"

This is an anonymized version of the code for the paper "Augmenting and Tuning Knowledge Graph Embeddings", currently under review for UAI 2019. A public version of this repository will be made available on GitHub when the paper is out of peer review. Until then, please do not distribute this code.

Quickstart

Set up and activate a python environment with Tensorflow. cd into the directory of this README.md file and run the following command:

CUDA_VISIBLE_DEVICES=0 nohup python3 src/train.py dat/FB15K out/fb15k-map \
    --model ComplEx \
    -k 2000 \
    -B 100 \
    --initial_reg_strength 0.02 \
    --optimizer adagrad \
    --lr0_mu 0.02 \
    --eval_dat both \
    --epochs_per_eval 5 \
    --steps_per_summary 1000 \
    --lr_exponent 0.5 \
    --lr_offset 120786 &
    # (120786 steps corresponds to 25 epochs)

This starts a background process that fits a point estimation of the ComplEx model to the FB15K data set using proportionally scaled regularizer strengths.

You can track the progress and predictive performances by reading the log file, or graphically in tensorboard:

tensorboard --logdir out

It shows evaluation metrics as scalars under the tabs "eval_valid" (for results on the validation set). By default, the script trains for 500 epochs, but this is rarely needed for convergence. The script automatically imitates early stopping by keeping a checkpoint of the training epoch with the highest MRR on the validation set around.

Variational EM

For variational EM, use the following command line options:

• --em: Activate variational EM.
• --lr0_sigma: (Initial) learning rate for the log standard deviations (we used the default value).
• --lr0_lambda: (Initial) learning rate for the hyperparameters (we used the default value).
• --initialize_from: Path to a checkpoint file with a pretrained model.

For a full list of available command line arguments, use --help.

Implementing Your Own Models

To implement your own model, derive from the class AbstractModel. Use the DistMult or ComplEx models, defined in the file src/distmult_model.py and src/complex_model.py as a template. Overwrite the functions define_emb to define the model parameters for entity and relation embeddings, and the function unnormalized_score to implement the score f.

Licences

Please see the file LICENSES.md in the dat directory for licenses of the packaged data sets.