Deep Learning based
Knowledge Extraction Toolkit

Suporting cnSchema, standard supervised setting, low-resource setting, document-level setting and multi-modal setting for knowledge base population

Introduction

To promote efficient Chinese knowledge graph construction, we provide DeepKE-cnSchema, a specific version of DeepKE, containing off-the-shelf models based on cnSchema. DeepKE-cnSchema supports multiple tasks such as Chinese entity extraction and relation extraction. It can extract 50 relation types and 28 entity types, including common entity types such as person, location, city, institution, etc and the common relation types such as ancestral home, birthplace, nationality and other types.

Entity Recognition

DeepKE(NER),  Roberta-wwm-ext,  Chinese

Google dowload

Baidu Netdisk download(password:u022)

Relation Extraction

DeepKE(RE),  Roberta-wwm-ext,  Chinese

Google dowload

Baidu Netdisk download(password:29oe)

Chinese Model Download

For entity recognition and relation extraction tasks, we provide models based on RoBERTa-wwm-ext, Chinese and BERT-wwm, Chinese respectively, convenient for users to utilize to extract knowledge.

Model Instructions

As for the entity extraction model, take pytoch version DeepKE(RE), RoBERTa-wwm-ext, Chinese as an example. After downloading, unzip the zip file to obtain the file structure as shown the left figure.


PyTorch version contains pytorch_ model. bin, config. json, vocab. txt file.


As for the relation extraction model, take pytoch version DeepKE(RE), RoBERTa-wwm-ext, Chinese as an example. The model is .pth file after downloading.

Entity Recognition


Model

P

R

F1

DeepKE(NER), RoBERTa-wwm-ext, Chinese

  0.80  

 0.86 

 0.83 

DeepKE(NER), BERT-wwm, Chinese

0.78

0.86

0.82

Relation Extraction


Model

P

R

F1

DeepKE  (RE), RoBERTa-wwm-ext, Chinese

  0.88  

  0.86  

  0.87  

DeepKE  (RE), BERT-wwm, Chinese

0.87

0.86

0.86

Chinese Baseline Performance

DeepKE fine-tunes based on chinese-roberta-wwm-ext and chinese-bert-wwm to obtain DeepKE-cnSchema(NER) and DeepKE-cnSchema(RE) models. Hyper-parameters used in the model are pre-defined. The performances on the test datasets are shown in the left Table.

Support cnSchema Types

DeepKE-cnSchema is an off-the-shelf version that supports the Chinese knowledge graphs construction. Cnschema is developed for Chinese information processing, which uses advanced knowledge graphs, natural language processing and machine learning technologies. It integrates structured text data, supports rapid domain knowledge modeling and open data automatic processing across data sources, domains and languages, and provides schema-level support and services for emerging application markets such as intelligent robots, semantic search and intelligent computing. Currently, the Schema types supported by DeepKE-cnSchema are as follows:

Entity Schema
  Serial Number     Entity Type     ID     Serial Number     Entity Type     ID     Serial Number     Entity Type     ID     Serial Number     Entity Type     ID  
1 人物 YAS 2 影视作品 TOJ 3 NGS 4 生物 QCV
5 Number OKB 6 Date BQF 7 国家 CAR 8 网站 ZFM
9 网络小说 EMT 10 图书作品 UER 11 歌曲 QEE 12 地点 UFT
13 气候 GJS 14 行政区 SVA 15 Text ANO 16 历史人物 KEJ
17 学校 ZDI 18 企业 CAT 19 出版社 GCK 20 书籍 FQK
21 音乐专辑 BAK 22 城市 RET 23 经典 QZP 24 电视综艺 QAQ
25 机构 ZRE 26 作品 TDZ 27 语言 CVC 28 学科专业 PMN
Relation Schema
  Serial Number     Head Entity Type     Tail Entity Type     Relation     Serial Number     Head Entity Type     Tail Entity Type     Relation     Serial Number     Head Entity Type     Tail Entity Type     Relation  
1 地点 人物 祖籍 2 人物 人物 父亲 3 地点 企业 总部地点
4 地点 人物 出生地 5 生物 6 Number 行政区 面积
7 Text 机构 简称 8 Date 影视作品 上映时间 9 人物 人物 妻子
10 音乐专辑 歌曲 所属专辑 11 Number 企业 注册资本 12 城市 国家 首都
10 音乐专辑 歌曲 所属专辑 11 Number 企业 注册资本 12 城市 国家 首都
13 人物 影视作品 导演 14 Text 历史人物 15 Number 人物 身高
16 企业 影视作品 出品公司 17 Number 学科专业 修业年限 18 Date 人物 出生日期
19 人物 影视作品 制片人 20 人物 人物 母亲 21 人物 影视作品 编辑
22 国家 人物 国籍 23 人物 影视作品 编剧 24 网站 网站小说 连载网络
25 人物 人物 丈夫 26 Text 历史人物 朝代 27 Text· 人物 民族
28 Text 历史人物 朝代 29 出版社 书籍 出版社 30 人物 电视综艺 主持人
31 Text 学科专业 专业代码 32 人物 歌曲 歌手 33 人物 歌曲 作曲
34 人物 网络小说 主角 35 人物 企业 董事长 36 Date 机构 成立时间
37 学校 人物 毕业院校 38 Number 机构 占地面积 39 语言 国家 官方语言
40 Text 行政区 邮政编码 41 Number 行政区 人口数量 42 Date 企业 成立日期
43 人物 图书作品 作者 44 Date 企业 成立日期 45 人物 歌曲 作曲
46 气候 气候 行政区 47 人物 电视综艺 嘉宾 48 人物 影视作品 主演
49 作品 影视作品 改编自 50 人物 企业 创始人

Model Quick Start

Named Entity Recognition (NER)


Users can directly download the model for usage. The details are as follows:

1. Create the downloaded folder as checkpoints

2. Modify the `get_labels`function in the source code. The returned labels are given in type.txt


  def get_labels(self):
      return ['O', 'B-YAS', 'I-YAS', 'B-TOJ', 'I-TOJ', 'B-NGS', 'I-NGS',
              'B-QCV', 'I-QCV', 'B-OKB', 'I-OKB', 'B-BQF', 'I-BQF', 'B-CAR',
              'I-CAR', 'B-ZFM', 'I-ZFM', 'B-EMT', 'I-EMT', 'B-UER', 'I-UER',
              'B-QEE', 'I-QEE', 'B-UFT', 'I-UFT', 'B-GJS', 'I-GJS', 'B-SVA',
              'I-SVA', 'B-ANO', 'I-ANO', 'B-KEJ', 'I-KEJ', 'B-ZDI', 'I-ZDI',
              'B-CAT', 'I-CAT', 'B-GCK', 'I-GCK', 'B-FQK', 'I-FQK', 'B-BAK',
              'I-BAK', 'B-RET', 'I-RET', 'B-QZP', 'I-QZP', 'B-QAQ', 'I-QAQ',
              'B-ZRE', 'I-ZRE', 'B-TDZ', 'I-TDZ', 'B-CVC', 'I-CVC', 'B-PMN',
              'I-PMN', '[CLS]', '[SEP]']
							

3. Modify the parameter text in predict.yaml to the text to be predicted

4. Make inference. The text and entity pairs to be predicted are fed to the program through the terminal.


  python predict.py
							

To use the pre-trained models, just input the sentence "《星空黑夜传奇》是连载于起点中文网的网络小说,作者是啤酒的罪孽". After running `python predict.py`, results can be obtained which show that the entity type "星空黑夜传奇" is "网络小说", "起点中文网" is "网站" and "啤酒的罪孽" is "人物".

Modify the parameter text in predict.yaml to change the text for inference.


  text=“《星空黑夜传奇》是连载于起点中文网的网络小说,作者是啤酒的罪孽”
							

Finally, output the results:


  NER句子:
  《星空黑夜传奇》是连载于起点中文网的网络小说,作者是啤酒的罪孽
  NER结果:
  [('星','B-UER'),('空','I-UER'),('黑','I-UER'),('夜','I-UER'),('传','I-UER'),
   ('奇','I-UER'),('起','B-ZFM'),('点','I-ZFM'),('中','I-ZFM'),('文','I-ZFM')
   ('网','I-ZFM'),('啤','B-YAS'),('酒','I-YAS'),('的','I-YAS'),('罪','I-YAS'),
   ('孽','I-YAS')]
							

Relation Extraction (RE)


Users can directly download the model for usage. The details are as follows:

1. Modify the parameter fp in predict.yaml to the path of downloaded file and num_relations in embedding.yaml to 51 (relation nums)

2. Make inference. The text and entity pairs to be predicted are fed to the program through the terminal.


  python predict.py
							

To use the pre-trained model, run python predict.py and input the sentence "歌曲《人生长路》出自刘德华国语专辑《男人的爱》,由李泉作词作曲,2001年出行发版". The given entity pair are "男人的爱" and "人生长路". Finally, the extracted relation is "所属专辑" aligned with cnschema.

To change the text to be predicted, modify the _get_predict_instance function in predict.py to the following example:


  def _get_predict_instance(cfg):
      flag = input('是否使用范例[y/n],退出请输入: exit .... ')
      flag = flag.strip().lower()
      if flag == 'y' or flag == 'yes':
          sentence = '歌曲《人生长路》出自刘德华国语专辑《男人的爱》,由李泉作词作曲
                      2001年出行发版'
          head = '男人的爱'
          tail = '人生长路'
          head_type = ''
          tail_type = ''
      elif flag == 'n' or flag == 'no':
          sentence = input('请输入句子:')
          head = input('请输入句中需要预测关系的头实体:')
          head_type = input('请输入头实体类型(可以为空,按enter跳过):')
          tail = input('请输入句中需要预测关系的尾实体:')
          tail_type = input('请输入尾实体类型(可以为空,按enter跳过):')
      elif flag == 'exit':
          sys.exit(0)
      else:
          print('please input yes or no, or exit!')
          _get_predict_instance()
							
      instance = dict()
      instance['sentence'] = sentence.strip()
      instance['head'] = head.strip()
      instance['tail'] = tail.strip()
      if head_type.strip() == '' or tail_type.strip() == '':
          cfg.replace_entity_with_type = False
          instance['head_type'] = 'None'
          instance['tail_type'] = 'None'
      else:
          instance['head_type'] = head_type.strip()
          instance['tail_type'] = tail_type.strip()
							
      return instance
							

Finally, output the results:


  “男人的爱”和“人生长路”在句中关系为“所属专辑”,置信度为0.99
							

Joint Entity and Relation Extraction


After aforementioned trained models are downloaded, entites and their relations in a text can be extracted together. If there are more than two entities in one sentence, some predicted entity pairs may be incorrect because these entity pairs are not in training sets and need to be exracted further. The detailed steps are as follows:

1. In conf, modify text in predict.yaml as the sentence to be predicted, nerfp as the directory of the trained NER model and refp as the directory of the trained RE model.

2. Predict


  python predict.py
							

Many results will be output. Take the input text "此外网易云平台还上架了一系列歌曲,其中包括田馥甄的《小幸运》等" as example.

(1) Output the result of NER: [('田', 'B-YAS'), ('馥', 'I-YAS'), ('甄', 'I-YAS'), ('小', 'B-QEE'), ('幸', 'I-QEE'), ('运', 'I-QEE')]

(2) Output the processed result: {'田馥甄': '人物', '小幸运': '歌曲'}

(3) Output the result of RE: "田馥甄" 和 "小幸运" 在句中关系为:"歌手",置信度为0.92

(4) Output the result as jsonld:


  {
    "@context": {
	  "歌手": "https://cnschema.openkg.cn/item/%E6%AD%8C%E6%89%8B/16693#viewPageContent"
    },
    "@id": "田馥甄",
    "歌手": {
      "@id": "小幸运"
    }
  }
							

Custom Models (Advanced Usage)

Support advance usage to obtain custom models with user-defined datasets

Named Entity Recognition (NER)


If you need to use customized dataset for training, follow the steps bellow:

1. Download customized dataset and put it into the data folder.

2. Modify the parameter bert_model in train.yaml of the conf folder to the specify model. Users can choose different models to train by modifying the yaml file.

3. Train.


  python run.py
				

Relation Extraction (RE)


If you need to use other models for training, follow the steps bellow:

1. Download the customized dataset and rename it to data.

2. Modify the parameter model_name in train.yaml of the conf folder to lm, num_relations in embedding_yaml to the number of relations(eg: 51). Users can choose different models to train by modifying the yaml file.

3. Train.


  python run.py
				

FAQ

Q: How to use this model?


A: It is off-the-shelf. After downloading the model, follow the instructions and you can extract the knowledge contained in the predefined cnSchema.

If you want to extract knowledge other than cnSchema, you can use the advanced version of customized data for training.

Q: Is there any other cnSchema extraction model available?


A: Unfortunately, we can only support part of knowledge extraction of cnSchema for the time being. More knowledge extraction models will be published in the future.

Q: Embedding error for customized dataset.


A: The Chinese data may contain invisible special characters, which cannot be encoded and thus an error is reported. You can preprocess the Chinese data through the editor or other tools to solve this problem.

Citation

If the resources or technologies in this project are helpful to your research work, you are welcome to cite the following papers in your thesis:


  @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 Liang, Xiaozhuan and others},
    journal={arXiv preprint arXiv:2201.03335},
    year={2022}
  }
								

Disclaimers

The contents of this project are only for technical research reference and shall not be used as any conclusive basis. Users can freely use the model within the scope of the license, but we are not responsible for the direct or indirect losses caused by the use of the project.

Problem Feedback

If you have any questions, please submit them in GitHub issue.