A Knowledge Graph Data Transformation Model for e- Government

Abstract

In the digital age, governments globally are leveraging technology to bolster their services and uplift citizen well-being. This surge has propelled the evolution of the e-Government realm, accompanied by heightened complexity, rendering it an ideal arena for exploring the widespread impact of Artificial Intelligence (AI) at large, and Knowledge Graph (KG) specifically. e-Government and AI now serve as strategic and tactical tools for governments worldwide, aiming to deliver public services with heightened efficiency, efficacy, and transparency. One area in e-Government that KG is used to address, is the challenge of creating a single knowledge source, in RDF graph data, from traditional data sources such as relational data. In this paper, we present a model for transforming data from relational to RDF, in an e- Government context. Our aim is to advance the e-Government objective of effective and efficient public service delivery and citizens engagement, given a complex e-Government instance. We focus on data transformation using RDB2RDF mapping language, a yml parser that converts mapping rules from yml to ttl turtle format. This output mapping rules is then used to transform the relational data to RDF data. Our research approach affords us the means to analyze and design our model; and validate and evaluate our work. Our model and the development approach help to achieve the e-Government goals of efficient and effective service delivery and citizens engagement.

Keywords

References