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Elementarisation method for public data based on urban knowledge systems
Studies | 更新时间：2024-08-07
|
Elementarisation method for public data based on urban knowledge systems
- Big data research Vol. 10, Issue 4, Pages: 130-148(2024)
- 作者机构：
1. 京东城市（北京）数字科技有限公司，北京 100176
2. 京东智能城市研究院，北京 100176
3. 西南交通大学计算机与人工智能学院，四川 成都 611756
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-0271.2024042
CLC： TP399-
Online First：2024-07，
Published：15 July 2024
- 稿件说明：
移动端阅览
郑宇, 易修文, 齐德康, 等. 基于城市知识体系的公共数据要素构建方法[J]. 大数据, 2024,10(4):130-148. DOI： 10.11959/j.issn.2096-0271.2024042.
ZHENG Yu, YI Xiuwen, QI Dekang, et al. Elementarisation method for public data based on urban knowledge systems. [J]. Big data research, 2024,10(4):130-148. DOI： 10.11959/j.issn.2096-0271.2024042.
郑宇, 易修文, 齐德康, 等. 基于城市知识体系的公共数据要素构建方法[J]. 大数据, 2024,10(4):130-148. DOI： 10.11959/j.issn.2096-0271.2024042. DOI：
ZHENG Yu, YI Xiuwen, QI Dekang, et al. Elementarisation method for public data based on urban knowledge systems. [J]. Big data research, 2024,10(4):130-148. DOI： 10.11959/j.issn.2096-0271.2024042. DOI：
Elementarisation method for public data based on urban knowledge systems
摘要
数据要素是数字经济发展的核心动能。城市公共数据的基础良好、普适性强、应用场景丰富，成为政府主导的数据要素的首选。当前数据与应用耦合，不同应用之间共享数据难，人工数据治理过程滞后、繁重低效，仅依靠自动抽取技术无法保证数据要素的精度。为此，基于人机智能协同的总体思路，提出基于城市知识体系的数据要素构建方法。首先，对大量城市业务进行解构和抽象，构建以人、地、事、物、组织5类实体，实体间关系及实体属性为核心的城市知识体系，并以这些实体、关系和属性为数据要素的原子描述，向上组合表达各种城市业务，向下形成可标准化的数据资源体系。其次，研发一套数字化控件，承载基于城市知识体系的数据要素化理论，通过灵活配置的方式开发服务于市民的各类应用，使数据在产生时就与城市知识体系关联，自动形成数据要素。最后，构建智能学习和推荐算法，更好地连接数字化控件和城市知识体系，使应用配置人员无须学习城市知识体系就能顺畅地使用数字化控件，降低了工具的使用门槛。该方法可大大提高公共数据要素产生的效率和扩大公共数据要素的规模，释放公共数据要素的价值。
Abstract
Data elements are the key momentum for boosting digital economy.The data generated by public services provided by governments (a.k.a.public data) is ready to be transferred into data elements
because it has been well organized in the past decade.Unfortunately
public data is strictly coupled with the systems generating them
making it difficult for different applications to share data.The process of munul data governance is lagging
heavy and inefficient
and relying on automatic extraction method can’t ensure the accuracy of data elements.To tackle these challenges
leveraging the synergy between human and machine intelligence
we propose an elementarisation method for public data based on urban knowledge system.Our method is comprised of an urban knowledge system
a set of digital controls and some machine learning algorithms.The urban knowledge system consists of entities
relationships between entities
and the properties associated with these entities and relationships
which can be used to construct different kinds of public services and form standard data representation that can be shared among different applications.Powered by the urban knowledge system
the digital controls enable governments to create different applications as public services flexibly
through a configurable way without writing any codes.Later
the information input by citizens through digital controls in these applications is transferred into data elements automatically.Finally
the machine learning algorithms assist users to use digital controls smoothly through intelligent recommendations.Our method can produce data elements automatically
efficiently and accurately
unlocking the value of data for digital economy.
关键词
Keywords
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