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🏖️ MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment.

<p align="center"><i><b>👆 Click to see the Video</b></i></p>

🔔 News

2024-03 Our paper NativE: Multi-modal Knowledge Graph Completion in the Wild [Repo] is accepted by SIGIR 2024 !.
2024-02 We preprint our Survey Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey [Repo].
2024-02 We release the [Repo] for our paper Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion , COLING 2024
2024-02 We preprint our Paper ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment [Repo].
2024-01 Our paper Revisit and Outstrip Entity Alignment: A Perspective of Generative Models [Repo] is accepted by ICLR 2024 !
2023-07 We release the [Repo] for our paper: Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment ! [Slide], ISWC 2023
2023-04 We release the complete code and data for MEAformer ! [Slide] [Vedio], ACM MM 2023

🔬 Dependencies

pip install -r requirement.txt

Details

Python (>= 3.7)
PyTorch (>= 1.6.0)
numpy (>= 1.19.2)
Transformers (== 4.21.3)
easydict (>= 1.10)
unidecode (>= 1.3.6)
tensorboard (>= 2.11.0)

🚀 Train

Quick start: Using script file (run.sh)

>> cd MEAformer
>> bash run.sh

Optional: Using the bash command

>> cd MEAformer
# -----------------------
# ---- non-iterative ----
# -----------------------
# ----  w/o surface  ---- 
# FBDB15K
>> bash run_meaformer.sh 1 FBDB15K norm 0.8 0 
>> bash run_meaformer.sh 1 FBDB15K norm 0.5 0 
>> bash run_meaformer.sh 1 FBDB15K norm 0.2 0 
# FBYG15K
>> bash run_meaformer.sh 1 FBYG15K norm 0.8 0 
>> bash run_meaformer.sh 1 FBYG15K norm 0.5 0 
>> bash run_meaformer.sh 1 FBYG15K norm 0.2 0 
# DBP15K
>> bash run_meaformer.sh 1 DBP15K zh_en 0.3 0 
>> bash run_meaformer.sh 1 DBP15K ja_en 0.3 0 
>> bash run_meaformer.sh 1 DBP15K fr_en 0.3 0
# ----  w/ surface  ---- 
# DBP15K
>> bash run_meaformer.sh 1 DBP15K zh_en 0.3 1 
>> bash run_meaformer.sh 1 DBP15K ja_en 0.3 1 
>> bash run_meaformer.sh 1 DBP15K fr_en 0.3 1
# -----------------------
# ------ iterative ------
# -----------------------
# ----  w/o surface  ---- 
# FBDB15K
>> bash run_meaformer_il.sh 1 FBDB15K norm 0.8 0 
>> bash run_meaformer_il.sh 1 FBDB15K norm 0.5 0 
>> bash run_meaformer_il.sh 1 FBDB15K norm 0.2 0 
# FBYG15K
>> bash run_meaformer_il.sh 1 FBYG15K norm 0.8 0 
>> bash run_meaformer_il.sh 1 FBYG15K norm 0.5 0 
>> bash run_meaformer_il.sh 1 FBYG15K norm 0.2 0 
# DBP15K
>> bash run_meaformer_il.sh 1 DBP15K zh_en 0.3 0 
>> bash run_meaformer_il.sh 1 DBP15K ja_en 0.3 0 
>> bash run_meaformer_il.sh 1 DBP15K fr_en 0.3 0
# ----  w/ surface  ---- 
# DBP15K
>> bash run_meaformer_il.sh 1 DBP15K zh_en 0.3 1 
>> bash run_meaformer_il.sh 1 DBP15K ja_en 0.3 1 
>> bash run_meaformer_il.sh 1 DBP15K fr_en 0.3 1

❗Tips: you can open the run_meaformer.sh or run_meaformer_il.sh file for parameter or training target modification.

🎯 Results

$\bf{H@1}$ Performance with the Settings: w/o surface & Non-iterative in UMAEA. We modified part of the MSNEA to involve not using the content of attribute values but only the attribute types themselves (See issues for details):

Method	$\bf{DBP15K_{ZH-EN}}$	$\bf{DBP15K_{JA-EN}}$	$\bf{DBP15K_{FR-EN}}$
MSNEA	.609	.541	.557
EVA	.683	.669	.686
MCLEA	.726	.719	.719
MEAformer	.772	.764	.771
UMAEA	.800	.801	.818

📚 Dataset

❗NOTE: Download from GoogleDrive (1.26G) and unzip it to make those files satisfy the following file hierarchy:

ROOT
├── data
│   └── mmkg
└── code
    └── MEAformer

Case analysis Jupyter script: GoogleDrive (180M) base on the raw images of entities (need to be unzip). I hope this gives you a good understanding of this dataset.
[ Option ] The raw Relations & Attributes appeared in DBP15k and case from MEAformer can be downloaded from Huggingface (150M).
[ Option ] The raw images of entities appeared in DBP15k can be downloaded from Baidu Cloud Drive (50GB) with the pass code mmea. All images are saved as title-image pairs in dictionaries and can be accessed with the following code :

import pickle
zh_images = pickle.load(open("eva_image_resources/dbp15k/zh_dbp15k_link_img_dict_full.pkl",'rb'))
print(en_images["http://zh.dbpedia.org/resource/香港有線電視"].size)

Code Path

<details> <summary>👈 🔎 Click</summary>

MEAformer
├── config.py
├── main.py
├── requirement.txt
├── run_meaformer.sh
├── run_meaformer_il.sh
├── run.sh
├── model
│   ├── __init__.py
│   ├── layers.py
│   ├── MEAformer_loss.py
│   ├── MEAformer.py
│   ├── MEAformer_tools.py
│   └── Tool_model.py
├── src
│   ├── __init__.py
│   ├── distributed_utils.py
│   ├── data.py
│   └── utils.py
└── torchlight
    ├── __init__.py
    ├── logger.py
    ├── metric.py
    └── utils.py

</details>

Data Path

<details> <summary>👈 🔎 Click</summary>

mmkg
├── DBP15K
│   ├── fr_en
│   │   ├── ent_ids_1
│   │   ├── ent_ids_2
│   │   ├── ill_ent_ids
│   │   ├── training_attrs_1
│   │   ├── training_attrs_2
│   │   ├── triples_1
│   │   └── triples_2
│   ├── ja_en
│   │   ├── ent_ids_1
│   │   ├── ent_ids_2
│   │   ├── ill_ent_ids
│   │   ├── training_attrs_1
│   │   ├── training_attrs_2
│   │   ├── triples_1
│   │   └── triples_2
│   ├── translated_ent_name
│   │   ├── dbp_fr_en.json
│   │   ├── dbp_ja_en.json
│   │   └── dbp_zh_en.json
│   └── zh_en
│       ├── ent_ids_1
│       ├── ent_ids_2
│       ├── ill_ent_ids
│       ├── training_attrs_1
│       ├── training_attrs_2
│       ├── triples_1
│       └── triples_2
├── FBDB15K
│   └── norm
│       ├── ent_ids_1
│       ├── ent_ids_2
│       ├── ill_ent_ids
│       ├── training_attrs_1
│       ├── training_attrs_2
│       ├── triples_1
│       └── triples_2
├── FBYG15K
│   └── norm
│       ├── ent_ids_1
│       ├── ent_ids_2
│       ├── ill_ent_ids
│       ├── training_attrs_1
│       ├── training_attrs_2
│       ├── triples_1
│       └── triples_2
├── embedding
│   └── glove.6B.300d.txt
├── pkls
│   ├── dbpedia_wikidata_15k_dense_GA_id_img_feature_dict.pkl
│   ├── dbpedia_wikidata_15k_norm_GA_id_img_feature_dict.pkl
│   ├── FBDB15K_id_img_feature_dict.pkl
│   ├── FBYG15K_id_img_feature_dict.pkl
│   ├── fr_en_GA_id_img_feature_dict.pkl
│   ├── ja_en_GA_id_img_feature_dict.pkl
│   └── zh_en_GA_id_img_feature_dict.pkl
├── MEAformer
└── dump

</details>

🤝 Cite:

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

@inproceedings{DBLP:conf/mm/ChenCZGFHZGPSC23,
  author       = {Zhuo Chen and
                  Jiaoyan Chen and
                  Wen Zhang and
                  Lingbing Guo and
                  Yin Fang and
                  Yufeng Huang and
                  Yichi Zhang and
                  Yuxia Geng and
                  Jeff Z. Pan and
                  Wenting Song and
                  Huajun Chen},
  title        = {MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality
                  Hybrid},
  booktitle    = {{ACM} Multimedia},
  pages        = {3317--3327},
  publisher    = {{ACM}},
  year         = {2023}
}

💡 Acknowledgement

We appreciate MCLEA, MSNEA, EVA, MMEA and many other related works for their open-source contributions.