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https://github.com/THUDM/SelfKG
Codes for WWW2022 accepted paper: SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
https://github.com/THUDM/SelfKG
entity-alignment self-supervised-learning
Last synced: about 2 months ago
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Codes for WWW2022 accepted paper: SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
- Host: GitHub
- URL: https://github.com/THUDM/SelfKG
- Owner: THUDM
- License: mit
- Created: 2021-06-17T10:32:53.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-11T08:39:51.000Z (about 2 years ago)
- Last Synced: 2024-07-08T00:48:43.919Z (2 months ago)
- Topics: entity-alignment, self-supervised-learning
- Language: Python
- Homepage:
- Size: 21 MB
- Stars: 66
- Watchers: 9
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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
"XXX file not found"
Please make sure you've downloaded all the dataset according to README.
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}
}
```