Authors :
Friday Orji; Nuka Nwiabu; Okoni Bennett; Onate Taylor
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/bx8c9ekd
Scribd :
https://tinyurl.com/2sjxs425
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR316
Abstract :
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.
Keywords :
Knowledge Graph, E-Government, Ontology, RDF, AI, OBDA, Architecture, Model, TOGAF, Data.
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.
Keywords :
Knowledge Graph, E-Government, Ontology, RDF, AI, OBDA, Architecture, Model, TOGAF, Data.
