Government agencies increasingly rely on data driven analysis to evaluate public policies, assess outcomes, and justify strategic decisions. At the same time, policy evaluation requires more than numerical indicators, as legal mandates, institutional knowledge, expert judgment, and contextual reasoning play a central role in public decision making. This review examines research on knowledge centric decision support systems designed to support policy evaluation in government services. The article synthesizes prior work across decision support systems, knowledge management, artificial intelligence, and public sector information systems to identify architectural patterns, reasoning mechanisms, and governance considerations relevant to policy analysis. By integrating these perspectives, the review highlights how knowledge centric approaches can enhance transparency, consistency, and institutional learning in government policy evaluation.
Pratama et al. (2020) studied this question.