A Framework for a Public Service Recommender System Based on Neuro-Symbolic AI

Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework for a public service recommender system that combines knowledge graphs (KGs) and large language models (LLMs) in a neuro-symbolic AI architecture. The framework expresses public service preconditions as machine-readable rules based on data standards and provides dynamic recommendations for public services based on citizens’ profiles through automated reasoning. LLMs are utilized to extract preconditions from unstructured textual regulations and create RDF-based evidence models, while KGs provide validation of preconditions through SHACL rules and explainable reasoning towards semantic interoperability. A prototype use case on students applying for housing allowance showcases the feasibility of the proposed framework. The analysis indicates that combining KGs with LLMs for identifying relevant public services for different citizens’ profiles can improve the quality of public services and reduce administrative burdens. This work contributes and promotes the proactive “No-Stop Government” model, where services are recommended to users without explicit requests. The findings highlight the promising potential of employing neuro-symbolic AI to transform e-government processes, while also addressing challenges related to legal complexity, privacy and data fragmentation for large-scale adoption.