A Knowledg Graph Model for e-Government

Many governments around the world have invested huge amount of resource to build their e- Government capabilities, to meet government objectives of effective public service delivery and citizens engagement. The increase in size of an e-Government landscape has led to the increase in complexity of the infrastructure. This increasing complex infrastructure presents a challenge for governments to continue to meet its objectives. Knowledge Graph (KG), a constituent AI technology, has shown a lot of promise in helping governments meet its objectives in the midst of the complexity. A major aspect of this complexity is the need to maintain a single view of the world, in the form of a unified meaning of data, within a given e-Government instance, given the heterogeneity in data models used in the different departments within an e-Government instance. In this paper, we present a unique perspective in addressing the problem of deriving semantic meaning from disparate data in an e-Government context, using KG. Our aim is to advance the objectives of effective service delivery and citizens engagement in a complex e- Government instance. We focus on creating a data- centric architectural model that is single, simple and extensible, based on KG. We create a functional model based on architectural view and viewpoints from standards such as The Open Group Architectural Framework (TOGAF). The functional model highlights the various components that underpin the functions. We have developed our model within the context of a Design Science Research (DSR) approach, and we provide evaluation of same model within that context. An e- Government KG model guides the development of KG solutions in e-Government, in order to achieve the e- Government enterprise goals of effective service delivery and citizens engagement.