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MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion

license AAAI Pytorch

Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity information, thereby facilitating the discovery of unobserved triples in the large-scale knowledge graphs (KGs). Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs. The missing modality information impedes modal interaction, consequently undermining the model's performance. In this paper, we propose a modality adversarial and contrastive framework (MACO) to solve the modality-missing problem in MMKGC. MACO trains a generator and discriminator adversarially to generate missing modality features that can be incorporated into the MMKGC model. Meanwhile, we design a cross-modal contrastive loss to improve the performance of the generator. Experiments on public benchmarks with further explorations demonstrate that MACO could achieve state-of-the-art results and serve as a versatile framework to bolster various MMKGC models.

🔔 News

🌈 Model Architecture

Model_architecture

🔬 Dependencies

  • python 3
  • torch >= 1.8.0
  • numpy
  • dgl-cu111 == 0.9.1
  • All experiments are performed with one A100-40G GPU.

📕 Code Path

  • MACO/ pretrain with MACO to complete the modality information. We have prepared the FB15K-237 dataset and the visual embeddings extracted with Vision Transformer (ViT). You should first download it from this link.

  • MMKGC/ run multi-modal KGC to evaluate the quality of generated visual features.

  • run MACO

cd MACO/
# download the FB15K-237 visual embeddings and put it in data/
# run the training code
python main.py
  • run MMKGC
cd MMKGC/
# Put the generated visual embedding in MACO to visual/ 
mv ../MACO/EMBEDDING_NAME visual/

# run the MMKGC model
DATA=FB15K237
NUM_BATCH=1024
KERNEL=transe
MARGIN=6
LR=2e-5
NEG_NUM=32
VISUAL=random-vit
MS=0.6
POSTFIX=2.0-0.01

CUDA_VISIBLE_DEVICES=0 nohup python run_ikrl.py -dataset=$DATA \
  -batch_size=$NUM_BATCH \
  -margin=$MARGIN \
  -epoch=1000 \
  -dim=128 \
  -save=./checkpoint/ikrl/$DATA-New-$KERNEL-$NUM_BATCH-$MARGIN-$LR-$VISUAL-large-$MS \
  -img_grad=False \
  -img_dim=768 \
  -neg_num=$NEG_NUM \
  -kernel=$KERNEL \
  -visual=$VISUAL \
  -learning_rate=$LR \
  -postfix=$POSTFIX \
  -missing_rate=$MS > ./log/IKRL$MS-$DATA-$KERNEL-4score-$MARGIN-$VISUAL-$MS-$POSTFIX.txt &

This is a simple demo to run IKRL model. The scripts to train other models (TBKGC, RSME) can be found in MMKGC/scripts/.

💡 Related Works

There are also some other works about multi-modal knowledge graphs from ZJUKG team. If you are interest in multi-modal knowledge graphs, you could have a look at them:

Multi-modal Entity Alignment

Multi-modal Knowledge Graph Completion

Knowledge Graph with Large Language Models

Open-source Tools

🤝 Cite:

Please condiser citing this paper if you use the code from our work. Thanks a lot :)

@article{zhang2023maco,
  title={MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion},
  author={Zhang, Yichi and Chen, Zhuo and Zhang, Wen},
  journal={arXiv preprint arXiv:2308.06696},
  year={2023}
}