README.md

MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion

Overview

🎆 News

• 2024-04 Our paper and code are released on ArXiV and Github.
• 2024-02 We preprint our Survey Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey [Repo].

Dependencies

pip install -r requirement.txt

Details

• Python==3.9
• numpy==1.24.2
• scikit_learn==1.2.2
• torch==2.0.0
• tqdm==4.64.1
• transformers==4.28.0

Data Preparation

You should first get the textual token embedding by running save_token_embedding.py with transformers library. The modality tokenization code will be released soon. You can first try MyGO on the pre-processed datasets DB15K and MKG-W. The modality tokenization needs to download the original raw multi-modal data therefore the code will need more time to be prepared.

Train and Evaluation

You can refer to the training scripts in run.sh to reproduce our experiment results. Here is an example for DB15K dataset.

CUDA_VISIBLE_DEVICES=0 nohup python train_mygo_fgc.py --data DB15K --num_epoch 1500 --hidden_dim 1024 --lr 1e-3 --dim 256 --max_vis_token 8 --max_txt_token 4 --num_head 2 --emb_dropout 0.6 --vis_dropout 0.3 --txt_dropout 0.1 --num_layer_dec 1 --mu 0.01 > log.txt &

More training scripts can be found in run.sh.

🤝 Citation

@misc{zhang2024mygo,
      title={MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion}, 
      author={Yichi Zhang and Zhuo Chen and Lingbing Guo and Yajing Xu and Binbin Hu and Ziqi Liu and Huajun Chen and Wen Zhang},
      year={2024},
      eprint={2404.09468},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}