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: about 5 hours 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 (about 5 years ago)
• Last Synced: 2024-08-03T09:07:17.073Z (3 months ago)
• Topics: entity-relation, information-extraction, knowledge-base, knowledge-extraction, open-domain, paper-implementations, python3, relation-extraction
• Language: Python
• Homepage:
• Size: 292 KB
• Stars: 523
• Watchers: 12
• Forks: 122
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• 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.