An Ontology Driven Machine Learning Applications in Public Policy Analysis: A Systematic Literature Review

Abstract

Abstract This systematic literature review aims to explore the role of ontology-driven machine learning applications in public policy analysis. The study employs the PRISMA methodology to identify and analyze relevant literature published between 2012 and 2022. The review includes studies that investigate the use of machine learning techniques in policy analysis, the integration of ontologies in machine learning models, and the potential of this approach in improving policy-making processes. The findings suggest that ontology-driven machine learning applications have great potential in enhancing the accuracy and efficiency of policy analysis, while also addressing the challenges and limitations of traditional methods. The review provides insights into the key domains, methods, and outcomes of studies on this topic and discusses the implications for future research and practice in public policy analysis.