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Codes for WWW2022 accepted paper: SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

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# SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs

Original implementation for paper SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs.

This paper is accepted and **nominated as a best paper** by  [The Web Conference2022](https://www2022.thewebconf.org/)! :satisfied:

SelfKG is the **first** **self-supervised** entity alignment method **without label supervision**, which can **match or achieve comparable results with state-of-the-art supervised baselines**. The performance of SelfKG suggests self-supervised learning offers great potential for entity alignment in Knowledge Graphs.

[SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs](https://arxiv.org/abs/2203.01044)
  
https://doi.org/10.1145/3485447.3511945

- [Installation](#installation)
  - [Requirements](#requirements)
- [Quick Start](#quick-start)
  - [Data Preparation](#data-preparation)
  - :star:[Run Experiments](#run-experiments)
- [❗ Common Issues](#-common-issues)
- [Citing SelfKG](#citing-selfkg)

## Installation

### Requirements

```txt
torch==1.9.0
faiss-cpu==1.7.1
numpy==1.19.2
pandas==1.0.5
tqdm==4.61.1
transformers==4.8.2
torchtext==0.10.0
```

You can use [`setup.sh`](https://github.com/THUDM/SelfKG/blob/main/setup.sh) to set up your Anaconda environment by

```bash
bash setup.sh
```

## Quick Start

### Data Preparation

You can download the our data from [here](https://zenodo.org/record/6326870#.YiI2K6tBxPY), and the final structure our project should be:

```bash
├── data
│   ├── DBP15K
│   │   ├── fr_en
│   │   ├── ja_en
│   │   └── zh_en
│   ├── DWY100K
│   │   ├── dbp_wd
│   │   └── dbp_yg
│   └── LaBSE
│       ├── bert_config.json
│       ├── bert_model.ckpt.index
│       ├── checkpoint
│       ├── config.json
│       ├── pytorch_model.bin
│       └── vocab.txt
│   └── getdata.sh
├── loader
├── model
├── run.sh # Please use this bash to run the experiments!
├── run_DWY_LaBSE_neighbor.py # SelfKG on DWY100k
├── run_LaBSE_neighbor.py # SelfKG on DBP15k
... # run_LaBSE_*.py # Ablation code will be available soon
├── script
│   └── preprocess
├── settings.py
└── setup.sh # Can be used to set up your Anaconda environment
```

You can also use the following scripts to download the datasets directly:

```bash
cd data
bash getdata.sh # The download speed is decided by your network connection. If it's pretty slow, please directly download the datasets from the website as mentioned before.
```

### :star:Run Experiments

**Please use**

**`bash run.sh`**

to reproduce our experiments results. For more details, please refer to [`run.sh`](https://github.com/THUDM/SelfKG/blob/main/run.sh) and our code.

## ❗ Common Issues

<details>
<summary>
"XXX file not found"
</summary>
<br/>
Please make sure you've downloaded all the dataset according to README.
</details>

to be continued ...

## Citing SelfKG

If you use SelfKG in your research or wish to refer to the baseline results, please use the following BibTeX.

```
@article{DBLP:journals/corr/abs-2203-01044,
  author    = {Xiao Liu and
               Haoyun Hong and
               Xinghao Wang and
               Zeyi Chen and
               Evgeny Kharlamov and
               Yuxiao Dong and
               Jie Tang},
  title     = {SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs},
  journal   = {CoRR},
  volume    = {abs/2203.01044},
  year      = {2022},
  url       = {https://arxiv.org/abs/2203.01044},
  eprinttype = {arXiv},
  eprint    = {2203.01044},
  timestamp = {Mon, 07 Mar 2022 16:29:57 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2203-01044.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

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