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Resources and code for paper "Fuzzy Logic based Logical Query Answering on Knowledge Graphs".

## Environment
Make sure your local environment has the following installed:

Python3.9
    torch == 1.9.0
    wandb == 0.9.7

Install the dependency using:

pip install -r requirements.txt

## Download data

Download data from [here](http://snap.stanford.edu/betae/KG_data.zip) and put it under `data` folder.

The directory structure should be like `[PROJECT_DIR]/data/NELL-betae/train-queries.pkl`.

Only FB15k-237 and NELL995 are used in our study.

## Train
Training script example: `./run.sh`

It usually takes 4 days to a week to finish a run on a NVIDIA® GP102 TITAN Xp (12GB) GPU.

*TODO: More training scripts for easy training will be added soon.*

## Test

The trained model will be automatically stored under the folder `./trained_models`. The model name will be `[WANDB_RUN_NAME].pt`.

To test a trained model, you can use the following command:

python ./test-pretrained-model.py [DATA_NAME] [WANDB_RUN_NAME]

By default, the test tests for product logic. You can also test for other logic systems ('godel' or 'luka') by modifying the `logic` variable in the script.

### Test the pretrained model

The pretrained FuzzQE model (product logic) for NELL can be downloaded [here](https://drive.google.com/file/d/15ByNcDayg5Vw67SaIk9ZPE3Gfa9tlTmo/view?usp=sharing). You can put it under `./trained_models` and use the following command to test it:

python ./test-pretrained-model.py NELL feasible-resonance-1518

*TODO: More pretrained models will be uploaded soon.*

## Reference
Please refer to our paper if you find the resources useful.

Xuelu Chen, Ziniu Hu, Yizhou Sun. Fuzzy Logic based Logical Query Answering on Knowledge Graphs. *Proceedings of the Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.*

@inproceedings{chen2021fuzzyqa,
        title={Fuzzy Logic based Logical Query Answering on Knowledge Graphs},
        author={Chen, Xuelu and Hu, Ziniu and Sun, Yizhou}
        booktitle={Proceedings of the Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI)},
        year={2022}
    }

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