# DeepKE **Repository Path**: wofe/deepke ## Basic Information - **Project Name**: DeepKE - **Description**: DeepKE是由浙江大学知识图谱团队维护开源知识图谱抽取工具集。 - **Primary Language**: Python - **License**: MIT - **Default Branch**: main - **Homepage**: http://deepke.openkg.cn/CN/index.html - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 192 - **Created**: 2023-01-16 - **Last Updated**: 2023-01-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Documentation PyPI GitHub Documentation Open In Colab

English | 简体中文

A Deep Learning Based Knowledge Extraction Toolkit
for Knowledge Graph Construction

[DeepKE](https://arxiv.org/pdf/2201.03335.pdf) 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](https://zjunlp.github.io/DeepKE/), [Google Colab tutorials](https://colab.research.google.com/drive/1vS8YJhJltzw3hpJczPt24O0Azcs3ZpRi?usp=sharing), [online demo](http://deepke.zjukg.cn/), [paper](https://arxiv.org/pdf/2201.03335.pdf), [slides](https://drive.google.com/file/d/1IIeIZAbVduemqXc4zD40FUMoPHCJinLy/view?usp=sharing) and [poster](https://drive.google.com/file/d/1vd7xVHlWzoAxivN4T5qKrcqIGDcSM1_7/view?usp=sharing) for beginners. **Reading Materials**: Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 ([Tutorial on CCKS 2022](http://sigkg.cn/ccks2022/?page_id=24)) \[[slides](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\] Efficient and Robust Knowledge Graph Construction ([Tutorial on AACL-IJCNLP 2022](https://www.aacl2022.org/Program/tutorials)) \[[slides](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\] Prompt Learning-related research works and toolkits for PLM-based KG Embedding Learning, Editing and Applications [[Resources](https://github.com/zjunlp/PromptKG)\] Reasoning with language model prompting \[[Survey](https://arxiv.org/abs/2212.09597)\]\[[Paper-list](https://github.com/zjunlp/Prompt4ReasoningPapers)\] **Related Toolkit**: [Doccano](https://github.com/doccano/doccano)、[MarkTool](https://github.com/FXLP/MarkTool)、[LabelStudio](https://labelstud.io/ )：Data Annotation Toolkits [LambdaKG](https://github.com/zjunlp/PromptKG/tree/main/lambdaKG): A library and benchmark for PLM-based KG embeddings # Table of Contents * [What's New](#whats-new) * [Prediction Demo](#prediction-demo) * [Model Framework](#model-framework) * [Quick Start](#quick-start) * [Requirements](#requirements) * [Introduction of Three Functions](#introduction-of-three-functions) * [1. Named Entity Recognition](#1-named-entity-recognition) * [2. Relation Extraction](#2-relation-extraction) * [3. Attribute Extraction](#3-attribute-extraction) * [Notebook Tutorial](#notebook-tutorial) * [Tips](#tips) * [To do](#to-do) * [Citation](#citation) * [Contributors](#contributors) * [Other Knowledge Extraction Open-Source Projects](#other-knowledge-extraction-open-source-projects)
# What's New ## Nov, 2022 * Add data [annotation instructions](https://github.com/zjunlp/DeepKE/blob/main/README_TAG.md) for entity recognition and relation extraction, automatic labelling of weakly supervised data ([entity extraction](https://github.com/zjunlp/DeepKE/tree/main/example/ner/prepare-data) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/prepare-data)), and optimize [multi-GPU training](https://github.com/zjunlp/DeepKE/tree/main/example/re/standard). ## Sept, 2022 * The paper [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://arxiv.org/abs/2201.03335) has been accepted by the EMNLP 2022 System Demonstration Track. ## Aug, 2022 * We have added [data augmentation](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot/DA) (Chinese, English) support for [low-resource relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot). ## June, 2022 * We have added multimodal support for [entity](https://github.com/zjunlp/DeepKE/tree/main/example/ner/multimodal) and [relation extraction](https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal). ## May, 2022 * We have released [DeepKE-cnschema](https://github.com/zjunlp/DeepKE/blob/main/README_CNSCHEMA.md) 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](https://arxiv.org/abs/2201.03335) ## 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](https://github.com/faressoft/terminalizer). Get the [code](https://drive.google.com/file/d/1r4tWfAkpvynH3CBSgd-XG79rf-pB-KR3/view?usp=share_link).
# 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 ```bash 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](https://github.com/zjunlp/DeepKE/tree/main/docker) with [tutorials](https://github.com/zjunlp/DeepKE/issues/145) please refer to the [Tips](#tips) to speed up installation** ```bash conda create -n deepke python=3.8 conda activate deepke ``` 1. Install *DeepKE* with source code (**Recommended**) ```bash python setup.py install python setup.py develop ``` 2. Install *DeepKE* with `pip` ```bash pip install deepke ``` **Step3** Enter the task directory ```bash cd DeepKE/example/re/standard ``` **Step4** Download the dataset, or follow the [annotation instructions](https://github.com/zjunlp/DeepKE/blob/main/README_TAG.md) to obtain data ```bash 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](https://docs.wandb.ai/quickstart)*. ```bash 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 used，such as `xxx/checkpoints/2019-12-03_ 17-35-30/cnn_ epoch21.pth`. ```bash python predict.py ``` - **❗NOTE: if you encounter any errors, please refer to the [Tips](#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](https://github.com/doccano/doccano), [MarkTool](https://github.com/FXLP/MarkTool), or they can use the [Weak Supervision](https://github.com/zjunlp/DeepKE/blob/main/example/ner/prepare-data) with DeepKE to obtain data automatically): | Sentence | Person | Location | Organization | | :----------------------------------------------------------: | :------------------------: | :------------: | :----------------------------: | | 本报北京9月4日讯记者杨涌报道：部分省区人民日报宣传发行工作座谈会9月3日在4日在京举行。 | 杨涌 | 北京 | 人民日报 | | 《红楼梦》由王扶林导演，周汝昌、王蒙、周岭等多位专家参与制作。 | 王扶林，周汝昌，王蒙，周岭 | | | | 秦始皇兵马俑位于陕西省西安市,是世界八大奇迹之一。 | 秦始皇 | 陕西省，西安市 | | - Read the detailed process in specific README - **[STANDARD (Fully Supervised)](https://github.com/zjunlp/DeepKE/tree/main/example/ner/standard)** ***We provide the off-the-shelf model, [DeepKE-cnSchema-NER](https://github.com/zjunlp/DeepKE/blob/main/README_CNSCHEMA_CN.md), which will extract entities in cnSchema without training.*** **Step1** Enter `DeepKE/example/ner/standard`. Download the dataset. ```bash 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. ```bash python run.py ``` **Step3** Prediction ```bash python predict.py ``` - **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot)** **Step1** Enter `DeepKE/example/ner/few-shot`. Download the dataset. ```bash 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. ```bash 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` ```bash python predict.py ``` - **[MULTIMODAL](https://github.com/zjunlp/DeepKE/tree/main/example/ner/multimodal)** **Step1** Enter `DeepKE/example/ner/multimodal`. Download the dataset. ```bash 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](https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py) and visual grounding via [onestage_grounding](https://github.com/zyang-ur/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.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. ```bash python run.py ``` **Step3** Prediction ```bash 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](https://github.com/doccano/doccano), [MarkTool](https://github.com/FXLP/MarkTool), or they can use the [Weak Supervision](https://github.com/zjunlp/DeepKE/blob/main/example/re/prepare-data) with DeepKE to obtain data automatically): | Sentence | Relation | Head | Head_offset | Tail | Tail_offset | | :----------------------------------------------------: | :------: | :--------: | :---------: | :--------: | :---------: | | 《岳父也是爹》是王军执导的电视剧，由马恩然、范明主演。 | 导演 | 岳父也是爹 | 1 | 王军 | 8 | | 《九玄珠》是在纵横中文网连载的一部小说，作者是龙马。 | 连载网站 | 九玄珠 | 1 | 纵横中文网 | 7 | | 提起杭州的美景，西湖总是第一个映入脑海的词语。 | 所在城市 | 西湖 | 8 | 杭州 | 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)](https://github.com/zjunlp/DeepKE/tree/main/example/re/standard)** ***We provide the off-the-shelf model, [DeepKE-cnSchema-RE](https://github.com/zjunlp/DeepKE/blob/main/README_CNSCHEMA_CN.md), which will extract relations in cnSchema without training.*** **Step1** Enter the `DeepKE/example/re/standard` folder. Download the dataset. ```bash 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. ```bash python run.py ``` **Step3** Prediction ```bash python predict.py ``` - **[FEW-SHOT](https://github.com/zjunlp/DeepKE/tree/main/example/re/few-shot)** **Step1** Enter `DeepKE/example/re/few-shot`. Download the dataset. ```bash 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.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. ```bash python run.py ``` **Step3** Prediction ```bash python predict.py ``` - **[DOCUMENT](https://github.com/zjunlp/DeepKE/tree/main/example/re/document)**
**Step1** Enter `DeepKE/example/re/document`. Download the dataset. ```bash 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.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. ```bash python run.py ``` **Step3** Prediction ```bash python predict.py ``` - **[MULTIMODAL](https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal)** **Step1** Enter `DeepKE/example/re/multimodal`. Download the dataset. ```bash 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](https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py) and visual grounding via [onestage_grounding](https://github.com/zyang-ur/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.yaml`as the path where the model trained last time was saved. And the path saving logs generated in training can be customized by `log_dir`. ```bash python run.py ``` **Step3** Prediction ```bash 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 | | :----------------------------------------------------------: | :------: | :------: | :--------: | :-----------: | :--------: | | 张冬梅，女，汉族，1968年2月生，河南淇县人 | 民族 | 张冬梅 | 0 | 汉族 | 6 | |诸葛亮，字孔明，三国时期杰出的军事家、文学家、发明家。| 朝代 | 诸葛亮 | 0 | 三国时期 | 8 | | 2014年10月1日许鞍华执导的电影《黄金时代》上映 | 上映时间 | 黄金时代 | 19 | 2014年10月1日 | 0 | - Read the detailed process in specific README - **[STANDARD (Fully Supervised)](https://github.com/zjunlp/DeepKE/tree/main/example/ae/standard)** **Step1** Enter the `DeepKE/example/ae/standard` folder. Download the dataset. ```bash 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. ```bash python run.py ``` **Step3** Prediction ```bash 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](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/standard/standard_ner_tutorial.ipynb) [NER Colab](https://colab.research.google.com/drive/1h4k6-_oNEHBRxrnzpxHPczO5SFaLS9uq?usp=sharing) [RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/standard/standard_re_pcnn_tutorial.ipynb) [RE Colab](https://colab.research.google.com/drive/1o6rKIxBqrGZNnA2IMXqiSsY2GWANAZLl?usp=sharing) [AE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ae/standard/standard_ae_tutorial.ipynb) [AE Colab](https://colab.research.google.com/drive/1pgPouEtHMR7L9Z-QfG1sPYkJfrtRt8ML) - Low-resource
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/few-shot/fewshot_ner_tutorial.ipynb) [NER Colab](https://colab.research.google.com/drive/1Xz0sNpYQNbkjhebCG5djrwM8Mj2Crj7F?usp=sharing) [RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/few-shot/fewshot_re_tutorial.ipynb) [RE Colab](https://colab.research.google.com/drive/1o1ly6ORgerkm1fCDjEQb7hsN5WKyg3JH?usp=sharing) - Document-level
[RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/document/document_re_tutorial.ipynb) [RE Colab](https://colab.research.google.com/drive/1RGUBbbOBHlWJ1NXQLtP_YEUktntHtROa?usp=sharing) - Multimodal
[NER Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/ner/multimodal/multimodal_ner_tutorial.ipynb) [NER Colab](https://colab.research.google.com/drive/1zOVZcn2v6xfCq39N80lbbHTdAfc7eGHb?usp=sharing) [RE Notebook](https://github.com/zjunlp/DeepKE/blob/main/tutorial-notebooks/re/multimodal/multimodal_re_tutorial.ipynb) [RE Colab](https://colab.research.google.com/drive/1ZRqqXMSDWoPpAKL7h9w9pq-SY-2gjc-u?usp=sharing)
# Tips 1.```Using nearest mirror```, **[THU](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/) in China, will speed up the installation of *Anaconda*; [aliyun](http://mirrors.aliyun.com/pypi/simple/) 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](https://github.com/zjunlp/DeepKE/tree/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](https://github.com/zjunlp/DeepKE/blob/main/example/re/standard/README.md)). 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,see[issue](https://github.com/zjunlp/DeepKE/issues/117) 6.More related low-resource knowledge extraction works can be found in [Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective](https://arxiv.org/pdf/2202.08063.pdf). 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 ```bibtex @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](https://person.zju.edu.cn/en/ningyu), 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](https://github.com/thredreams) Alibaba Group: Feiyu Xiong, Qiang Chen DAMO Academy: Zhenru Zhang, Chuanqi Tan, Fei Huang # Other Knowledge Extraction Open-Source Projects - [CogIE](https://github.com/jinzhuoran/CogIE) - [OpenNRE](https://github.com/thunlp/OpenNRE) - [OmniEvent](https://github.com/THU-KEG/OmniEvent) - [OpenUE](https://github.com/zjunlp/OpenUE) - [OpenIE](https://stanfordnlp.github.io/CoreNLP/openie.html) - [RESIN](https://github.com/RESIN-KAIROS/RESIN-pipeline-public) - [ZShot](https://github.com/IBM/zshot) - [ZS4IE](https://github.com/BBN-E/ZS4IE) - [OmniEvent](https://github.com/THU-KEG/OmniEvent)