WN18：
WN18 is a well-known KG which is originally extracted from WordNet. You can download here or here.
FB15K:
FB15K is a widely used dataset in KG embedding extracted from Freebase. You can download here.
WN18-IMG：
WN18-IMG is an extended dataset of WN18 [4] which prepares 10 images for each entity. Due to copyright reasons, it is not possible to give images directly in this article. You can obtain WN18-IMG by the following methods.
Entity images in WN18 can be obtained from ImageNet, and the specific entity image addresses can be obtained from Dolt. The specific steps are as follows:
1.Install Dolt
Dolt is a SQL database that you can fork, clone, branch, merge, push and pull just like a git repository. To install on Linux or Mac based systems run this command in your terminal:
sudo bash -c 'curl -L https://github.com/dolthub/dolt/releases/latest/download/install.sh | bash'
2.Clone ImageNet
dolt clone dolthub/image-net
3.Start dolt sql server
dolt sql-server &
4.Create conda environment
conda create --name RSME python=3.7
conda activate RSME
conda install pymysql
conda install requests
conda install Pillow
5.Run export scripts
python ./tools/export_urls.py --entIDs=./data/wn_enties
python ./tools/image_downloader.py --output_dir=./data/wn8 --entLinks=./data/ent_links --threads_num=5
FB15K-IMG：
mmkb provides a list of URLs that can be downloaded with a script which also scales the images (thanks to https://github.com/jrieke).
This project implements 4 types of image Encoder, Vision Transformer, Resnet50, VGG16, PHash. you can use them from image_encoder.py.
conda install pytorch-gpu
pip install pytorch_pretrained_vit
conda install imagehash
python image_encoder.py
The filter gate serves to filter images from the dataset level which are potentially incorrect, and it is implemented infilter_gate.py.
MRP is used to calculate the importance of pictures to relations, which is implemented in MRP.py.
