Artificial Intelligence in e-Government: A Computational Perspective Review
Abstract
e-Government is a field that has been significantly impacted by the ubiquity of Artificial Intelligence (AI). Specifically, Knowledge Graph (KG), a constituent technology of AI, is having a significant impact in e-Government. e-Government is defined as the application of information technology in government, and KG is a directed, labelled, multi-relational graph with some form of semantics. KG is used in advancing the e-Government objectives of effective and efficient service delivery and citizens engagement, given the increasing complexities of e- Government instances. Focus of AI in e-Government has evolved from the logic-based approaches to addressing the e-Government challenges, to the data-centric approaches. The logic-based approaches are driven mainly by the work done in the semantic web field, and the data-centric approaches are driven by work done in the machine learning field. Research activities have evolved from the logic-based to the data-centric approaches, and lately to the combination of both approaches. The AI in e-Government field could use a review of research trend in this niche research field, as a way to provide an indicative research outlook. This article attempts to provide such a review. First, it provides an overview of previous work carried-out in AI in e-Government, and then examines the different strands of research in the field including data management, intelligent web services and machine learning. Then it attempts to make a coherent statement of research direction in the field.
