Information Ecosystem Reengineering via Public Sector Knowledge Representation

TL;DR

This paper introduces a novel ontology-driven approach called Representation Disentanglement to improve knowledge representation and decision-making in public sector information ecosystems, supporting transparency and AI integration.

Contribution

It proposes a theoretically grounded, ontology-based framework to disentangle complex knowledge layers, enhancing explainability and traceability in public sector reengineering efforts.

Findings

Framework supports explainability and traceability

Enhances decision workflow transparency

Facilitates AI-driven governance processes

Abstract

Information Ecosystem Reengineering (IER) -- the technological reconditioning of information sources, services, and systems within a complex information ecosystem -- is a foundational challenge in the digital transformation of public sector services and smart governance platforms. From a semantic knowledge management perspective, IER becomes especially entangled due to the potentially infinite number of possibilities in its conceptualization, namely, as a result of manifoldness in the multi-level mix of perception, language and conceptual interlinkage implicit in all agents involved in such an effort. This paper proposes a novel approach -- Representation Disentanglement -- to disentangle these multiple layers of knowledge representation complexity hindering effective reengineering decision making. The approach is based on the theoretically grounded and implementationally robust ontology-driven conceptual modeling paradigm which has been widely adopted in systems analysis and (re)engineering. We argue that such a framework is essential to achieve explainability, traceability and semantic transparency in public sector knowledge representation and to support auditable decision workflows in governance ecosystems increasingly driven by Artificial Intelligence (AI) and data-centric architectures.