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[NeurIPS 2024] Official resources of "Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction".

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# Text2NKG

Official resources of **"Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction"**. Haoran Luo, Haihong E, Yuhao Yang, Tianyu Yao, Yikai Guo, Zichen Tang, Wentai Zhang, Shiyao Peng, Kaiyang Wan, Meina Song, Wei Lin, Yifan Zhu, Luu Anh Tuan. **NeurIPS 2024** \[[paper](https://proceedings.neurips.cc/paper_files/paper/2024/hash/305b2288122d46bf0641bdd86c9a7921-Abstract-Conference.html)\].

## Overview
![](https://raw.githubusercontent.com/LHRLAB/Text2NKG/main/figs/F0.drawio.png)
![](https://raw.githubusercontent.com/LHRLAB/Text2NKG/main/figs/F1.drawio.png)
![](https://raw.githubusercontent.com/LHRLAB/Text2NKG/main/figs/F2.drawio.png)

## Setup

### Default implementation environment

* *Linux(SSH) + Python3.7.13 + Pytorch1.8.1 + Cuda11.1*

```
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
```

### Install Dependencies

Install dependencies, The code is based on huggaface's [transformers](https://github.com/huggingface/transformers):
```
pip install -r requirement.txt
pip install --editable ./transformers
```
Then, we should install [apex](https://github.com/NVIDIA/apex).
```
unzip apex.zip
cd apex
python setup.py install
cd ..
```

### Datasets
Our experiments are based on HyperRED dataset.

### Download PLMs

Download Pre-trained Language Models from [Hugging Face](https://huggingface.co/): 
```
mkdir -p bert_models/bert-base-uncased
wget -P bert_models/bert-base-uncased https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin
wget -P bert_models/bert-base-uncased https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt
wget -P bert_models/bert-base-uncased https://huggingface.co/bert-base-uncased/resolve/main/config.json
```
```
mkdir -p bert_models/bert-large-uncased
wget -P bert_models/bert-large-uncased https://huggingface.co/bert-large-uncased/resolve/main/pytorch_model.bin
wget -P bert_models/bert-large-uncased https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt
wget -P bert_models/bert-large-uncased https://huggingface.co/bert-large-uncased/resolve/main/config.json
wget -P bert_models/bert-large-uncased https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json
wget -P bert_models/bert-large-uncased https://huggingface.co/bert-large-uncased/resolve/main/tokenizer_config.json
```
## Training

Train Text2NKG n-ary RE Models:
```
python run_re.py
```

## BibTex

If you find this work is helpful for your research, please cite:

```bibtex
@inproceedings{luo2024text2nkg,
 author = {Luo, Haoran and E, Haihong and Yang, Yuhao and Yao, Tianyu and Guo, Yikai and Tang, Zichen and Zhang, Wentai and Peng, Shiyao and Wan, Kaiyang and Song, Meina and Lin, Wei and Zhu, Yifan and Luu, Anh Tuan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {27417--27439},
 publisher = {Curran Associates, Inc.},
 title = {Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/305b2288122d46bf0641bdd86c9a7921-Paper-Conference.pdf},
 volume = {37},
 year = {2024}
}

```

For further questions, please contact: haoran.luo@ieee.org.

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