Code for the SIGIR 2022 paper "Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion"

Overview

MKGFormer

Code for the SIGIR 2022 paper "Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion"

Model Architecture

Illustration of MKGformer for (a) Unified Multimodal KGC Framework and (b) Detailed M-Encoder.

Requirements

To run the codes, you need to install the requirements:

pip install -r requirements.txt

Data Collection

The datasets that we used in our experiments are as follows:

  • Twitter2017

    You can download the twitter2017 dataset via this link (https://drive.google.com/file/d/1ogfbn-XEYtk9GpUECq1-IwzINnhKGJqy/view?usp=sharing)

    For more information regarding the dataset, please refer to the UMT repository.

  • MRE

    The MRE dataset comes from MEGA, many thanks.

    You can download the MRE dataset with detected visual objects using folloing command:

    cd MRE
    wget 120.27.214.45/Data/re/multimodal/data.tar.gz
    tar -xzvf data.tar.gz
  • MKG

    • FB15K-237-IMG

      For more information regarding the dataset, please refer to the mmkb and kg-bert repositories.

    • WN18-IMG

      For more information regarding the dataset, please refer to the RSME repository.

The expected structure of files is:

MKGFormer
 |-- MKG	# Multimodal Knowledge Graph
 |    |-- dataset       # task data
 |    |-- data          # data process file
 |    |-- lit_models    # lightning model
 |    |-- models        # mkg model
 |    |-- scripts       # running script
 |    |-- main.py   
 |-- MNER	# Multimodal Named Entity Recognition
 |    |-- data          # task data
 |    |-- models        # mner model
 |    |-- modules       # running script
 |    |-- processor     # data process file
 |    |-- utils
 |    |-- run_mner.sh
 |    |-- run.py
 |-- MRE    # Multimodal Relation Extraction
 |    |-- data          # task data
 |    |-- models        # mre model
 |    |-- modules       # running script
 |    |-- processor     # data process file
 |    |-- run_mre.sh
 |    |-- run.py

How to run

  • MKG Task

    • First run Image-text Incorporated Entity Modeling to train entity embedding.
        cd MKG
        bash scripts/pretrain_fb15k-237-image.sh
    • Then do Missing Entity Prediction.
        bash scripts/fb15k-237-image.sh
  • MNER Task

    To run mner task, run this script.

    cd MNER
    bash run_mner.py
  • MRE Task

    To run mre task, run this script.

    cd MRE
    bash run_mre.py

Acknowledgement

The acquisition of image data for the multimodal link prediction task refer to the code from https://github.com/wangmengsd/RSME, many thanks.

Papers for the Project & How to Cite

If you use or extend our work, please cite the paper as follows:

Owner
ZJUNLP
A NLP & KG Group of Zhejiang University
ZJUNLP
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