English |

A Deep Learning Based Knowledge Extraction Toolkit
for Knowledge Graph Construction

DeepKE is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema****low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction. We provide documents, Google Colab tutorials, online demo, paper, slides and poster for beginners.

Reading Materials:

Data-Efficient Knowledge Graph Construction, (Tutorial on CCKS 2022) [slides]

Efficient and Robust Knowledge Graph Construction (Tutorial on AACL-IJCNLP 2022) [slides]

Prompt Learning-related research works and toolkits for PLM-based KG Embedding Learning, Editing and Applications [Resources]

Reasoning with language model prompting [Survey][Paper-list]

Related Toolkit:

Doccano MarkTool LabelStudioData Annotation Toolkits

LambdaKG: A library and benchmark for PLM-based KG embeddings

What's New

Nov, 2022

Add data annotation instructions for entity recognition and relation extraction, automatic labelling of weakly supervised data (entity extraction and relation extraction), and optimize multi-GPU training.

Sept, 2022

The paper DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population has been accepted by the EMNLP 2022 System Demonstration Track.

Aug, 2022

We have added data augmentation (Chinese, English) support for low-resource relation extraction.

June, 2022

We have added multimodal support for entity and relation extraction.

May, 2022

We have released DeepKE-cnschema with off-the-shelf knowledge extraction models.

Jan, 2022

We have released a paper DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

Dec, 2021

We have added dockerfile to create the enviroment automatically.

Nov, 2021

The demo of DeepKE, supporting real-time extration without deploying and training, has been released.
The documentation of DeepKE, containing the details of DeepKE such as source codes and datasets, has been released.

Oct, 2021

pip install deepke
The codes of deepke-v2.0 have been released.

Aug, 2019

The codes of deepke-v1.0 have been released.

Aug, 2018

The project DeepKE startup and codes of deepke-v0.1 have been released.

Prediction Demo

There is a demonstration of prediction. The GIF file is created by Terminalizer. Get the code.

Model Framework

DeepKE contains a unified framework for named entity recognition, relation extraction and attribute extraction, the three knowledge extraction functions.
Each task can be implemented in different scenarios. For example, we can achieve relation extraction in standard, low-resource (few-shot), document-level and multimodal settings.
Each application scenario comprises of three components: Data including Tokenizer, Preprocessor and Loader, Model including Module, Encoder and Forwarder, Core including Training, Evaluation and Prediction.

Quick Start

DeepKE supports pip install deepke.
Take the fully supervised relation extraction for example.

Step1 Download the basic code

git clone --depth 1 https://github.com/zjunlp/DeepKE.git

Step2 Create a virtual environment using Anaconda and enter it.

NOTE: We provide a Dockerfile with tutorials please refer to the Tips to speed up installation

conda create -n deepke python=3.8

conda activate deepke

Install DeepKE with source code (Recommended)

python setup.py install

python setup.py develop

Install DeepKE with pip
```
pip install deepke
```

Step3 Enter the task directory

cd DeepKE/example/re/standard

Step4 Download the dataset, or follow the annotation instructions to obtain data

wget 120.27.214.45/Data/re/standard/data.tar.gz

tar -xzvf data.tar.gz

Many types of data formats are supported,and details are in each part.

Step5 Training (Parameters for training can be changed in the conf folder)

We support visual parameter tuning by using wandb.

python run.py

Step6 Prediction (Parameters for prediction can be changed in the conf folder)

Modify the path of the trained model in predict.yaml.The absolute path of the model needs to be usedsuch as xxx/checkpoints/2019-12-03_ 17-35-30/cnn_ epoch21.pth.

python predict.py

NOTE: if you encounter any errors, please refer to the Tips or submit a GitHub issue.

Requirements

python == 3.8

torch == 1.5
hydra-core == 1.0.6
tensorboard == 2.4.1
matplotlib == 3.4.1
transformers == 3.4.0
jieba == 0.42.1
scikit-learn == 0.24.1
pytorch-transformers == 1.2.0
seqeval == 1.2.2
tqdm == 4.60.0
opt-einsum==3.3.0
wandb==0.12.7
ujson

Introduction of Three Functions

1. Named Entity Recognition

Named entity recognition seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, organizations, etc.
The data is stored in .txt files. Some instances as following (Users can label data based on the tools Doccano, MarkTool, or they can use the Weak Supervision with DeepKE to obtain data automatically):

Sentence Person Location Organization

94934

,
Read the detailed process in specific README
- STANDARD (Fully Supervised)
  
  We provide the off-the-shelf model, DeepKE-cnSchema-NER, which will extract entities in cnSchema without training.
  
  Step1 Enter DeepKE/example/ner/standard. Download the dataset.
```
wget 120.27.214.45/Data/ner/standard/data.tar.gz

tar -xzvf data.tar.gz
```
  Step2 Training
  
  The dataset and parameters can be customized in the data folder and conf folder respectively.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```
- FEW-SHOT
  
  Step1 Enter DeepKE/example/ner/few-shot. Download the dataset.
```
wget 120.27.214.45/Data/ner/few_shot/data.tar.gz

tar -xzvf data.tar.gz
```
  Step2 Training in the low-resouce setting
  
  The directory where the model is loaded and saved and the configuration parameters can be cusomized in the conf folder.
```
python run.py +train=few_shot
```
  Users can modify load_path in conf/train/few_shot.yaml to use existing loaded model.
  
  Step3 Add - predict to conf/config.yaml, modify loda_path as the model path and write_path as the path where the predicted results are saved in conf/predict.yaml, and then run python predict.py
```
python predict.py
```
- MULTIMODAL
  
  Step1 Enter DeepKE/example/ner/multimodal. Download the dataset.
```
wget 120.27.214.45/Data/ner/multimodal/data.tar.gz

tar -xzvf data.tar.gz
```
  We use RCNN detected objects and visual grounding objects from original images as visual local information, where RCNN via faster_rcnn and visual grounding via onestage_grounding.
  
  Step2 Training in the multimodal setting
  - The dataset and parameters can be customized in the data folder and conf folder respectively.
  - Start with the model trained last time: modify load_path in conf/train.yamlas the path where the model trained last time was saved. And the path saving logs generated in training can be customized by log_dir.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```

2. Relation Extraction

Relationship extraction is the task of extracting semantic relations between entities from a unstructured text.
The data is stored in .csv files. Some instances as following (Users can label data based on the tools Doccano, MarkTool, or they can use the Weak Supervision with DeepKE to obtain data automatically):

Sentence Relation Head Head_offset Tail Tail_offset

1 8

1 7

8 2
!NOTE: If there are multiple entity types for one relation, entity types can be prefixed with the relation as inputs.
Read the detailed process in specific README
- STANDARD (Fully Supervised)
  
  We provide the off-the-shelf model, DeepKE-cnSchema-RE, which will extract relations in cnSchema without training.
  
  Step1 Enter the DeepKE/example/re/standard folder. Download the dataset.
```
wget 120.27.214.45/Data/re/standard/data.tar.gz

tar -xzvf data.tar.gz
```
  Step2 Training
  
  The dataset and parameters can be customized in the data folder and conf folder respectively.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```
- FEW-SHOT
  
  Step1 Enter DeepKE/example/re/few-shot. Download the dataset.
```
wget 120.27.214.45/Data/re/few_shot/data.tar.gz

tar -xzvf data.tar.gz
```
  Step 2 Training
  - The dataset and parameters can be customized in the data folder and conf folder respectively.
  - Start with the model trained last time: modify train_from_saved_model in conf/train.yamlas the path where the model trained last time was saved. And the path saving logs generated in training can be customized by log_dir.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```
- DOCUMENT
  
  Step1 Enter DeepKE/example/re/document. Download the dataset.
```
wget 120.27.214.45/Data/re/document/data.tar.gz

tar -xzvf data.tar.gz
```
  Step2 Training
  - The dataset and parameters can be customized in the data folder and conf folder respectively.
  - Start with the model trained last time: modify train_from_saved_model in conf/train.yamlas the path where the model trained last time was saved. And the path saving logs generated in training can be customized by log_dir.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```
- MULTIMODAL
  
  Step1 Enter DeepKE/example/re/multimodal. Download the dataset.
```
wget 120.27.214.45/Data/re/multimodal/data.tar.gz

tar -xzvf data.tar.gz
```
  We use RCNN detected objects and visual grounding objects from original images as visual local information, where RCNN via faster_rcnn and visual grounding via onestage_grounding.
  
  Step2 Training
  - The dataset and parameters can be customized in the data folder and conf folder respectively.
  - Start with the model trained last time: modify load_path in conf/train.yamlas the path where the model trained last time was saved. And the path saving logs generated in training can be customized by log_dir.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```

3. Attribute Extraction

Attribute extraction is to extract attributes for entities in a unstructed text.
The data is stored in .csv files. Some instances as following:

Sentence Att Ent Ent_offset Val Val_offset

19682 0 6

0 8

2014101 19 2014101 0
Read the detailed process in specific README
- STANDARD (Fully Supervised)
  
  Step1 Enter the DeepKE/example/ae/standard folder. Download the dataset.
```
wget 120.27.214.45/Data/ae/standard/data.tar.gz

tar -xzvf data.tar.gz
```
  Step2 Training
  
  The dataset and parameters can be customized in the data folder and conf folder respectively.
```
python run.py
```
  Step3 Prediction
```
python predict.py
```

Notebook Tutorial

This toolkit provides many Jupyter Notebook and Google Colab tutorials. Users can study DeepKE with them.

Standard Setting

NER Notebook

NER Colab

RE Notebook

RE Colab

AE Notebook

AE Colab
Low-resource

NER Notebook

NER Colab

RE Notebook

RE Colab
Document-level

RE Notebook

RE Colab
Multimodal

NER Notebook

NER Colab

RE Notebook

RE Colab

Tips

1.Using nearest mirror, THU in China, will speed up the installation of Anaconda; aliyun in China, will speed up pip install XXX.

2.When encountering ModuleNotFoundError: No module named 'past'run pip install future .

3.It's slow to install the pretrained language models online. Recommend download pretrained models before use and save them in the pretrained folder. Read README.md in every task directory to check the specific requirement for saving pretrained models.

4.The old version of DeepKE is in the deepke-v1.0 branch. Users can change the branch to use the old version. The old version has been totally transfered to the standard relation extraction (example/re/standard).

5.It's recommended to install DeepKE with source codes. Because user may meet some problems in Windows system with 'pip',and the source code modification will not work,seeissue

6.More related low-resource knowledge extraction works can be found in Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective.

7.Make sure the exact versions of requirements in requirements.txt.

To do

In next version, we plan to add event extraction to the toolkit.

Meanwhile, we will offer long-term maintenance to fix bugs, solve issues and meet new requests. So if you have any problems, please put issues to us.

Citation

Please cite our paper if you use DeepKE in your work

@article{zhang2022deepke,
  title={DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population},
  author={Zhang, Ningyu and Xu, Xin and Tao, Liankuan and Yu, Haiyang and Ye, Hongbin and Qiao, Shuofei and Xie, Xin and Chen, Xiang and Li, Zhoubo and Li, Lei and others},
  journal={arXiv preprint arXiv:2201.03335},
  year={2022}
}

Contributors

Zhejiang University: Ningyu Zhang, Liankuan Tao, Xin Xu, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Peng Wang, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Wen Zhang, Guozhou Zheng, Huajun Chen

Community Contributors: thredreams, eltociear

Alibaba Group: Feiyu Xiong, Qiang Chen

DAMO Academy: Zhenru Zhang, Chuanqi Tan, Fei Huang

Other Knowledge Extraction Open-Source Projects

Sentence	Person	Location	Organization
94934

,

Sentence	Relation	Head	Head_offset	Tail	Tail_offset
			1		8
			1		7
			8		2

Sentence	Ent_offset	Val	Val_offset
19682	0		6
	0		8
2014101	19	2014101	0

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