NativE: Multi-modal Knowledge Graph Completion in the Wild

• NativE: Multi-modal Knowledge Graph Completion in the Wild (ACM Library)
• NativE: Multi-modal Knowledge Graph Completion in the Wild (Arxiv)

To address the diversity and imbalance issues in multi-modal knowledge graph completion, we propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities and employs a collaborative modality adversarial training framework to augment the imbalanced modality information. We construct a new benchmark called WildKGC with five datasets to evaluate our method. The empirical results compared with 21 recent baselines confirm the superiority of our method, consistently achieving state-of-the-art performance across different datasets and various scenarios while keeping efficient and generalizable.

Overview

Dependencies

pip install -r requirement.txt

Details

• Python==3.8
• numpy==1.23.3
• scikit_learn==1.1.2
• torch==1.9.1
• tqdm==4.64.1 Our code is based on OpenKE, an open-source KGC project. You can refer to the OpenKE repo to build the environment.

Data Download

You should first download the pre-trained multi-modal embeddings from Google Drive and put them in the embeddings/ path.

Train and Evaluation

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

DATA=DB15K
EMB_DIM=250
NUM_BATCH=1024
MARGIN=12
LR=1e-4
LRG=1e-4
NEG_NUM=128
MU=0.0001
EPOCH=1000

CUDA_VISIBLE_DEVICES=1 nohup python run_adv_wgan_gp_3modal.py -dataset=$DATA \
  -batch_size=$NUM_BATCH \
  -margin=$MARGIN \
  -epoch=$EPOCH \
  -dim=$EMB_DIM \
  -adv_num=$ADV\
  -save=$DATA-$NUM_BATCH-$EMB_DIM-$NEG_NUM-$MU-$MARGIN-$LR-$EPOCH \
  -neg_num=$NEG_NUM \
  -mu=$MU \
  -learning_rate=$LR\
  -lrg=$LRG > $DATA-$EMB_DIM-$NUM_BATCH-$NEG_NUM-$MU-$MARGIN-$LR-$EPOCH.txt &

More training scripts can be found in scripts/.

🤝 Cite:

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

@inproceedings{DBLP:conf/sigir/ZhangCGXHLZC24,
  author       = {Yichi Zhang and
                  Zhuo Chen and
                  Lingbing Guo and
                  Yajing Xu and
                  Binbin Hu and
                  Ziqi Liu and
                  Wen Zhang and
                  Huajun Chen},
  title        = {NativE: Multi-modal Knowledge Graph Completion in the Wild},
  booktitle    = {{SIGIR}},
  pages        = {91--101},
  publisher    = {{ACM}},
  year         = {2024}
}