Code & data accompanying the paper "Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks".

This code is written in python 3. You will need to install a few python packages in order to run the code. We recommend you to use virtualenv to manage your python packages and environments. Please take the following steps to create a python virtual environment.

• If you have not installed virtualenv, install it with pip install virtualenv.
• Create a virtual environment with virtualenv venv.
• Activate the virtual environment with source venv/bin/activate.
• Install the package requirements with pip install -r requirements.txt.

In order to compute the meteor score, please download the required data from here and put it under the src/core/evaluation/meteor/data folder.

• Download the pretrained GloVe word ebeddings glove.840B.300d.zip and move glove.840B.300d.txt to the data folder in this repo.
• Download the data from here and move it to the data folder in this repo.
• Cd into the src folder
• Run the QG model and report the performance

      python main.py -config config/mhqg-wq/graph2seq.yml
      python main.py -config config/mhqg-pq/graph2seq.yml
  

• You can finetune the above trained QG model using RL by running the following command:

      python main.py -config config/mhqg-wq/rl_graph2seq.yml
      python main.py -config config/mhqg-pq/rl_graph2seq.yml
  

• You can find the output data in the out_dir folder specified in the config file.

If you found this code useful, please consider citing the following paper:

Chen, Yu, Lingfei Wu, and Mohammed J. Zaki. "Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks." arXiv preprint arXiv:2004.06015 (2020).

@article{chen2020toward,
  title={Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks},
  author={Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.},
  journal={arXiv preprint arXiv:2004.06015},
  year={2020}
}