NeuralKG

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[Tool] For Knowledge Graph Representation Learning

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<h1 align="center">
 An Open Source Library for Diverse Representation Learning of Knowledge Graphs
</h1>

 English | <a href="https://github.com/zjukg/NeuralKG/blob/main/README_CN.md">中文</a>

NeuralKG is a python-based library for diverse representation learning of knowledge graphs implementing **Conventional KGEs**, **GNN-based KGEs**, and **Rule-based
KGEs**. We provide [comprehensive documents](https://zjukg.github.io/NeuralKG/index.html) for beginners and an [online website](http://neuralkg.zjukg.org/) to organize an open and shared KG representation learning community.

# Table of Contents

- [Table of Contents](#table-of-contents)
- [😃What's New](#whats-new)
- [Overview](#overview)
- [Demo](#demo)
- [Implemented KGEs](#implemented-kges)
- [Quick Start](#quick-start)
 - [Installation](#installation)
 - [Training](#training)
 - [Evaluation](#evaluation)
 - [Hyperparameter Tuning](#hyperparameter-tuning)
- [Reproduced Results](#reproduced-results)
- [Notebook Guide](#notebook-guide)
- [Detailed Documentation](#detailed-documentation)
- [Citation](#citation)
- [NeuralKG Core Team](#neuralkg-core-team)

# 😃What's New
* [Apr, 2023] We propose a [challenge](https://tianchi.aliyun.com/competition/entrance/532081/introduction) about inductive KGRL at [CCKS2023](https://sigkg.cn/ccks2023/en/). Baseline is built based on [NeuralKG-ind](https://github.com/zjukg/NeuralKG/tree/ind). Welcome to involve!
* [Apr, 2023] We release [NeuralKG-ind](https://github.com/zjukg/NeuralKG/tree/ind) for inductive Knowledge Graph Representation Learning(KGRL). The [paper](https://arxiv.org/pdf/2304.14678.pdf) of NeuralKG-ind is accepted by SIGIR2023 in Demonstration Track. 
* [Jan, 2023] We add the [SEGNN](https://arxiv.org/abs/2109.11800) model for our library
* [Oct, 2022] We add the [DualE](https://ojs.aaai.org/index.php/AAAI/article/view/16850) model for our library
* [Sep, 2022] We add the [PairRE](https://arxiv.org/pdf/2011.03798.pdf) model for our library
* [Jun, 2022] We add the [HAKE](https://arxiv.org/abs/1911.09419) model for our library
* [Mar, 2022] We have provided [Google Colab Tutotials](https://drive.google.com/drive/folders/1OyuxvdjRNFzRuheNZaGGCsPe75T1pW1P?usp=sharing) help users use our library
* [Mar, 2022] We have provided a new [blog](http://neuralkg.zjukg.org/uncategorized/neuralkg-for-recommendation%ef%bf%bc/) about how to use NeuralKG on custom datasets
* [Feb, 2022] We have released a paper [NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs](https://arxiv.org/abs/2202.12571), which is accepted by SIGIR2022 in Demonstration Track.

# Overview

NeuralKG is built on [PyTorch Lightning](https://www.pytorchlightning.ai/). It provides a general workflow of diverse representation learning on KGs and is highly modularized, supporting three series of KGEs. It has the following features:

+  **Support diverse types of methods.** NeuralKG, as a library for diverse representation learning of KGs, provides implementations of three series of KGE methods, including **Conventional KGEs**, **GNN-based KGEs**, and **Rule-based KGEs**.

+ **Support easy customization.** NeuralKG contains fine-grained decoupled modules that are commonly used in different KGEs, including KG Data Preprocessing, Sampler for negative sampling, Monitor for hyperparameter tuning, Trainer covering the training, and model validation.

+ **long-term technical maintenance.** The core team of NeuralKG will offer long-term technical maintenance. Other developers are welcome to pull requests.

# Demo

There is a demonstration of NeuralKG.

<img src="pics/demo.gif">

# Implemented KGEs

|Components| Models |
|:---|:--------------:|
|KGEModel|[TransE](https://papers.nips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html), [TransH](https://ojs.aaai.org/index.php/AAAI/article/view/8870), [TransR](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9571/9523/), [ComplEx](http://proceedings.mlr.press/v48/trouillon16.pdf), [DistMult](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ICLR2015_updated.pdf), [RotatE](https://arxiv.org/abs/1902.10197), [ConvE](https://arxiv.org/abs/1707.01476), [BoxE](https://arxiv.org/pdf/2007.06267.pdf), [CrossE](https://arxiv.org/abs/1903.04750), [SimplE](https://arxiv.org/abs/1802.04868), [HAKE](https://arxiv.org/abs/1911.09419), [PairRE](https://arxiv.org/pdf/2011.03798.pdf), [DualE](https://ojs.aaai.org/index.php/AAAI/article/view/16850)|
|GNNModel|[RGCN](https://arxiv.org/abs/1703.06103), [KBAT](https://arxiv.org/abs/1906.01195), [CompGCN](https://arxiv.org/abs/1906.01195), [XTransE](https://link.springer.com/chapter/10.1007/978-981-15-3412-6_8), [SEGNN](https://arxiv.org/abs/2109.11800)||
|RuleModel|[ComplEx-NNE+AER](https://aclanthology.org/P18-1011/), [RUGE](https://arxiv.org/abs/1711.11231), [IterE](https://arxiv.org/abs/1903.08948)|

# Quick Start

## Installation

**Step1** Create a virtual environment using ```Anaconda``` and enter it
```bash
conda create -n neuralkg python=3.8
conda activate neuralkg
```
**Step2** Install the appropriate PyTorch and DGL according to your cuda version

Here we give a sample installation based on cuda == 11.1

+  Install PyTorch
```
pip install torch==1.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
```
+ Install DGL
```
pip install dgl-cu111 dglgo -f https://data.dgl.ai/wheels/repo.html
```

**Step3** Install package
+ From Pypi
```bash
pip install neuralkg
```

+ From Source

```bash
git clone https://github.com/zjukg/NeuralKG.git
cd NeuralKG
python setup.py install
```
## Training
```
# Use bash script
sh ./scripts/your-sh

# Use config
python main.py --load_config --config_path <your-config>

```

## Evaluation
```
python main.py --test_only --checkpoint_dir <your-model-path>
```
## Hyperparameter Tuning
NeuralKG utilizes [Weights&Biases](https://wandb.ai/site) supporting various forms of hyperparameter optimization such as grid search, Random search, and Bayesian optimization. The search type and search space are specified in the configuration file in the format "*.yaml" to perform hyperparameter optimization.

The following config file displays hyperparameter optimization of the TransE on the FB15K-237 dataset using bayes search:
```
command:
 - ${env}
 - ${interpreter}
 - ${program}
 - ${args}
program: main.py
method: bayes
metric:
 goal: maximize
 name: Eval|hits@10
parameters:
 dataset_name:
 value: FB15K237
 model_name:
 value: TransE
 loss_name:
 values: [Adv_Loss, Margin_Loss]
 train_sampler_class:
 values: [UniSampler, BernSampler]
 emb_dim:
 values: [400, 600]
 lr:
 values: [1e-4, 5e-5, 1e-6]
 train_bs:
 values: [1024, 512]
 num_neg:
 values: [128, 256]
```

# Reproduced Results
There are some reproduced model results on FB15K-237 dataset using NeuralKG as below. See more results in [here](https://zjukg.github.io/NeuralKG/result.html)

|Method | MRR | Hit@1 | Hit@3 | Hit@10 |
|:------:|:---:|:-----:|:-----:|:------:|
|TransE|0.32|0.23|0.36|0.51|
|TransR|0.23|0.16|0.26|0.38|
|TransH|0.31|0.2|0.34|0.50|
|DistMult|0.30|0.22|0.33|0.48|
|ComplEx|0.25|0.17|0.27|0.40|
|SimplE|0.16|0.09|0.17|0.29|
|ConvE|0.32|0.23|0.35|0.50|
|RotatE|0.33|0.23|0.37|0.53|
|BoxE|0.32|0.22|0.36|0.52|
|HAKE|0.34|0.24|0.38|0.54|
|PairRE|0.35|0.25|0.38|0.54|
|DualE|0.33|0.24|0.36|0.52|
|XTransE|0.29|0.19|0.31|0.45|
|RGCN|0.25|0.16|0.27|0.43|
|KBAT*|0.28|0.18|0.31|0.46|
|CompGCN|0.34|0.25|0.38|0.52|
|SEGNN|0.36|0.27|0.39|0.54|
|IterE|0.26|0.19|0.29|0.41|

*:There is a label leakage error in KBAT, so the corrected result is poor compared with the paper result. Details in https://github.com/deepakn97/relationPrediction/issues/28

# Notebook Guide

😃We use colab to provide some notebooks to help users use our library.

[![Colab Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/drive/folders/1OyuxvdjRNFzRuheNZaGGCsPe75T1pW1P?usp=sharing)

# Detailed Documentation
https://zjukg.github.io/NeuralKG/neuralkg.html

# Citation

Please cite our paper if you use NeuralKG in your work

```bibtex
@inproceedings{neuralkg,
  author    = {Wen Zhang and
               Xiangnan Chen and
               Zhen Yao and
               Mingyang Chen and
               Yushan Zhu and
               Hongtao Yu and
               Yufeng Huang and
               Yajing Xu and
               Ningyu Zhang and
               Zezhong Xu and
               Zonggang Yuan and
               Feiyu Xiong and
               Huajun Chen},
  title     = {NeuralKG: An Open Source Library for Diverse Representation Learning
               of Knowledge Graphs},
  booktitle = {{SIGIR}},
  pages     = {3323--3328},
  publisher = {{ACM}},
  year      = {2022}
}

```

# NeuralKG Core Team 
Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Peng Ye, Yichi Zhang, Ningyu Zhang, Guozhou Zheng, Haofen Wang, Huajun Chen

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