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https://github.com/lemonhu/open-entity-relation-extraction
Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.
https://github.com/lemonhu/open-entity-relation-extraction
entity-relation information-extraction knowledge-base knowledge-extraction open-domain paper-implementations python3 relation-extraction
Last synced: 6 days ago
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Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.
- Host: GitHub
- URL: https://github.com/lemonhu/open-entity-relation-extraction
- Owner: lemonhu
- License: mit
- Created: 2018-06-19T10:20:10.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-26T11:50:50.000Z (over 5 years ago)
- Last Synced: 2024-12-07T13:10:19.209Z (14 days ago)
- Topics: entity-relation, information-extraction, knowledge-base, knowledge-extraction, open-domain, paper-implementations, python3, relation-extraction
- Language: Python
- Homepage:
- Size: 292 KB
- Stars: 530
- Watchers: 12
- Forks: 122
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - lemonhu/open-entity-relation-extraction
README
# open-entity-relation-extraction
Knowledge triples extraction (entities and relations extraction) and knowledge base construction based on dependency syntax for open domain text.
基于依存句法分析,实现面向开放域文本的知识三元组抽取(实体和关系抽取)及知识库构建。
Welcome to watch, star or fork.
## Example
> "中国国家主席习近平访问韩国,并在首尔大学发表演讲"
We can extract knowledge triples from the sentence as follows:
- (中国, 国家主席, 习近平)
- (习近平, 访问, 韩国)
- (习近平, 发表演讲, 首尔大学)## Project Structure
```
knowledge_extraction/
|-- code/ # code directory
| |-- bean/
| |-- core/
| |-- demo/ # procedure entry
| |-- tool/
|-- data/ # data directory
| |-- input_text.txt # input text file
| |-- knowledge_triple.json # output knowledge triples file
|-- model/ # ltp models, can be downloaded from http://ltp.ai/download.html, select ltp_data_v3.4.0.zip
|-- resource # dictionaries dirctory
|-- requirements.txt # dependent python libraries
|-- README.md # project description
```## Requirements
This repo was tested on Python 3.5+. The requirements are:
- jieba>=0.39
- pyltp>=0.2.1## Quickstart
```shell
cd ./code/demo/
python extract_demo.py
```## Seven DSNF paradigms
![DSNF](./img/DSNF.png)
## References
If you use the code, please kindly cite the following paper:
Jia S, Li M, Xiang Y. Chinese Open Relation Extraction and Knowledge Base Establishment[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2018, 17(3): 15.