Awesome
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}
}