lemonhu / open-entity-relation-extraction

Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.

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

"**国家主席***访问韩国，并在首尔大学发表演讲"

We can extract knowledge triples from the sentence as follows:

• (**, 国家主席, ***)
• (***, 访问, 韩国)
• (***, 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

cd ./code/demo/
python extract_demo.py

Seven DSNF paradigms

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.