大数据 ›› 2024, Vol. 10 ›› Issue (4): 130-148.doi: 10.11959/j.issn.2096-0271.2024042

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基于城市知识体系的公共数据要素构建方法

郑宇1,2,3, 易修文1,2, 齐德康1,3, 潘哲逸1,2   

  1. 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
  • 出版日期:2024-07-01 发布日期:2024-07-01
  • 作者简介:郑宇(1979- ),男,博士,京东集团副总裁、京东智能城市研究院院长、京东科技首席数据科学家,IEEE Fellow,美国计算机学会杰出科学家,上海交通大学讲座教授,南京大学、香港科技大学等多所高校客座教授。先后担任人工智能顶尖国际期刊ACM TIST的主编、国家重点研发计划项目首席科学家及总负责人,以及ICDE及CIKM等多个国际会议的程序委员会主席。
    易修文(1991- ),男,博士,京东城市数据科学家,入选2021年度北京市科技新星计划,主要研究方向为城市大数据智能。
    齐德康(1993- ),男,西南交通大学计算机与人工智能学院博士生,主要研究方向为城市计算。
    潘哲逸(1992- ),男,博士,京东智能城市研究院研究员,在国际顶级期刊或会议上发表论文10余篇,主要研究方向为城市计算、时空数据挖掘、深度学习。
  • 基金资助:
    国家自然科学基金项目(62076191);北京市科技计划(Z211100004121008)

Elementarisation method for public data based on urban knowledge systems

Yu ZHENG1,2,3, Xiuwen YI1,2, Dekang QI1,3, Zheyi PAN1,2   

  1. 1 JD Intelligent Cities Technology Co., Ltd., Beijing 100176, China
    2 JD Intelligent Cities Research, BDA, Beijing 100176, China
    3 School of Computing and Artificial Intelligent, Southwest Jiaotong University, Chengdu 611756, China
  • Online:2024-07-01 Published:2024-07-01
  • Supported by:
    The National Natural Science Foundation of China(62076191);Beijing Science and Technology Plan(Z211100004121008)

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。

关键词: 数据要素, 数据资源体系, 城市计算, 城市知识体系

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.

Key words: data elements, data resource system, urban computing, urban knowledge system

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