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lcilp's Introduction

Locality-aware subgraphs for inductive link prediction in knowledge graphs

Dependencies

All the required packages can be installed by running

pip install -r requirements.txt

Usage

To start training the Clust-LP model (on WN18RR v1 as an example), run the following command:

python train.py -d WN18RR_v1 -e grail_wn_v1

To test the model, run the following commands:

- python test_auc.py -d WN18RR_v1_ind -e exp_wn_v1
- python test_ranking.py -d WN18RR_v1_ind -e exp_wn_v1

Acknowledgements

This work was supported by MEMEX project funded by the European Union's Horizon 2020 research and innovation program under grant agreement No 870743.

The code is implemented based on GraIL (https://github.com/kkteru/grail). Thanks for their code sharing.

lcilp's People

Contributors

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lcilp's Issues

Local run adaptations

Hello hebatef!

Thank you very much for sharing your code. I've been working with your repository to replicate and adapt it to other datasets. However, I had to modify some code to get it to run properly, as it was giving me errors otherwise.

I'm leaving here the PR with the modifications I had to make to get it running on my PC in case you want to integrate it:

Best regards!

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