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marvel_kg's Introduction

漫威知识图谱 Marvel knowledge graph

知识图谱概况

  • 数据源：中文维基百科、百度百科、漫威人物关系数据库

  • 实体：100+漫威人物

  • 属性：中文/英文名、性别、登场作品、所属团队、角色经历、能力值等

  • 关系三元组：320+

一.配置环境

  1. 配置环境以及安装neo4j，python版本需为3.6
  2. 控制台输入 "neo4j console" 启动neo4j
  3. 修改“KG_Marvel\neo_db\config.py”中的用户名密码
  4. 解压“KG_Marvel\KGQA\ltp_data_v3.4.0.zip”
  5. 修改“KG_Marvel\KGQA\ltp.py”中的模型路径（需用绝对路径）
  6. 运行“KG_Marvel\neo_db\creat_graph.py”，将关系传入数据库
  7. 运行 "KG_Marvel\app.py"
  8. 浏览器输入“http://localhost:5000/” 进入知识图谱。

二.文件描述

  • KGQA: 储存了基于ltp模型的分词函数
  • neo_db: 建立neo4j数据库
  • spyder：储存超级英雄个人信息和图片以及关系
  • static: 网页中用到的图片字体等
  • templates: 网页脚本
  • app.py: 启动知识图谱

三.算法说明

1.事实抽取

  • crawl存放爬虫文件，get_hlm_character.py、spider_url.py等进行信息抽取，外网数据.py为后期信息补充
  • 初始数据处理文件夹记录了数据清洗过程

2.知识融合

  • wikispyder找到维基百科中漫威领域对应数据
  • 根据词向量距离进行知识融合

3.类别推断

  • Type_Inference.py进行基于规则的类别推断

4.知识问答

  • FLAT模型包含FLAT命名实体识别模型
  • ltp.py为LTP中文分词库的调用实现

5.推荐系统

  • WMSeg-sota分词包含WMSeg分词算法的实现
  • word2vec.ipynb计算词向量，找到最合适的推荐实体

四.部分可视化展示

词云

全部关系图

单人物关系网

知识问答

marvel_kg's People

Contributors

luciferbobo avatar

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