Elementarisation method for public data based on urban knowledge systems

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.

Keywords

• data elements;data resource system;urban computing;urban knowledge system