Grapher is an end-to-end multi-stage Knowledge Graph (KG) construction system, that separates the overall generation process into two stages.
To run this code, please install PyTorch and Pytorch Lightning (we tested the code on Pytorch 1.13 and Pytorch Lightning 1.8.1)
Install dependencies
# clone project
git clone [email protected]:IBM/Grapher.git
# navigate to the directory
cd Grapher
# clone an external repository for reading the data
git clone https://gitlab.com/webnlg/corpus-reader.git corpusreader
# clone another external repositories for scoring the results
git clone https://github.com/WebNLG/WebNLG-Text-to-triples.git WebNLG_Text_to_triplesWebNLG 3.0 dataset
# download the dataset
git clone https://gitlab.com/shimorina/webnlg-dataset.gitThere are two scripts to run two versions of the algorithm
# naviagate to scripts directory
cd scripts
# run Grapher with the edge generation head
bash train_gen.sh
# run Grapher with the classifier edge head
bash train_class.sh# run the test on experiment "webnlg_version_1" using latest checkpoint last.ckpt
python main.py --run test --version 1 --default_root_dir output --data_path webnlg-dataset/release_v3.0/en
# run the test on experiment "webnlg_version_1" using checkpoint at iteration 5000
python main.py --run test --version 1 --default_root_dir output --data_path webnlg-dataset/release_v3.0/en --checkpoint_model_id 5000Results can be visualized in Tensorboard
tensorboard --logdir output@inproceedings{grapher2022,
title={Knowledge Graph Generation From Text},
author={Igor Melnyk, Pierre Dognin, Payel Das},
booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP)},
year={2022}
}