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SelfKG

(★ 76)

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

File Explorer

Download Latest Version (.zip)

- getdata.sh
- combine.png
- Figure2.PNG
- Figure3.PNG
- DBP15k.py
- DBP15kLoader.py
- DBP15kRawLoader.py
- DBP15KRawNeighbors.py
- DWY100K.py
- DWY100KRawNeighbor.py
- DWY_Neighbor.py
- Neighbors.py
- layers_DWY_LaBSE_neighbor.py
- layers_LaBSE_neighbor.py
- layers_LaBSE_SSL.py
- layers_LaBSE_SSL_DWY.py
- - deal_dataset.py
  - deal_raw_dataset.py
  - DWY_neighbor_token.py
  - get_DWY_token.py
  - get_token.py
  - labse_dump.py
  - neighbor_token.py
LICENSE
README.md
requirements.txt
run.sh
run_DWY_LaBSE_neighbor.py
run_LaBSE_neighbor.py
settings.py
setup.sh

# Installation Guide

1. Get the code

git clone https://github.com/THUDM/SelfKG

Downloads the entire project code from GitHub to your computer.

cd SelfKG

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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<img src="img/combine.png" style="zoom:100%;" />

<p align="center"><a href="https://github.com/THUDM/SelfKG/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/THUDM/SelfKG" /></a>

# 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}
}
```