Information Ecosystem Reengineering via Public Sector Knowledge Representation

2025-08-21

AI Summary

Digital transformation of public-sector information services faces challenges in restructuring the information ecosystem, stemming from deep coupling among heterogeneous stakeholders at perceptual, linguistic, and conceptual levels—resulting in high epistemic uncertainty that undermines semantic interoperability and decision traceability. To address this, we propose Representation Disentanglement: the first application of representation disentanglement to public-sector knowledge modeling. It hierarchically decouples knowledge complexity across perceptual, semantic, and ontological dimensions, enabling structured separation of knowledge representations. Integrated with ontology-driven conceptual modeling and semantic re-engineering, the approach yields a modeling architecture balancing theoretical rigor and engineering feasibility. Empirical evaluation demonstrates significant improvements in semantic transparency, decision interpretability, and audit verifiability of digital governance systems. The method establishes a traceable, verifiable knowledge infrastructure for intelligent governance platforms, advancing foundational support for accountable and explainable AI-enabled public administration.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Disentangling complex knowledge representation layers in public sector
• Addressing infinite conceptualization possibilities in information ecosystem reengineering
• Achieving explainable and auditable AI-driven governance decision workflows

Innovation

• Representation Disentanglement for knowledge complexity
• Ontology-driven conceptual modeling paradigm
• Explainable AI and semantic transparency governance