一个解决方案和实践,用于结合多源异质数据,构建企业知识图
Chenwei Yan1,2, Xinyue Fang3, Xiaotong Huang1,2
1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.
我们开发了一个框架,从各种数据源中构建企业知识图,增强人工智能基础设施. 这种方法提高了数据质量,并扩展了知识图.
科学领域:
- 人工智能的人工智能
- 知识工程知识工程知识工程
- 数据科学数据科学数据科学
背景情况:
- 从多个来源的异质数据构建高质量的域知识图带来了重大挑战.
- 知识图是人工智能应用程序的重要基础设施.
- 现有的方法往往难以有效地整合各种数据类型.
研究的目的:
- 通过整合结构化和非结构化数据,提出构建域知识图的综合框架.
- 提高知识图的质量和延长知识图的生命周期.
- 在企业尽职调查中展示企业知识图的实际应用.
主要方法:
- 开发了一个完整的过程框架,包括数据处理,信息提取,知识融合,数据存储和更新策略.
- 企业知识图的综合企业注册,诉讼和公告信息.
- 改进了从非结构化文本中提取三位数的现有模型,达到F1得分72.77%.
主要成果:
- 构建了一个企业知识图,有1,430,000个节点和3,170,000个边缘.
- 针对多源异质数据,应用了信息提取和数据存储的特定方法.
- 进行了对图形数据库的比较分析.
结论:
- 拟议的框架为域知识图构建提供了一个实际的解决方案.
- 开发的企业知识图部署并用于企业尽职调查.
- 及时更新和信息化的知识图表满足关键的业务需求.
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