Abstract

Driven by ‘success stories’ reported by private sector firms, government agencies have also started adopting various Artificial Intelligence (AI) technologies in diverse domains (e.g. health, taxation, and education); however, extensive research is required in order to exploit the full potential of AI in the public sector, and leverage various AI technologies to address important problems/needs. This paper makes a contribution in this direction: it presents a novel approach, as well as the architecture of an ICT platform supporting it, for the advanced exploitation of a specific AI technology, namely chatbots, in the public sector in order to address a crucial issue: the improvement of communication between government and citizens (which has for long time been problematic). The proposed approach builds on natural language processing, machine learning and data mining technologies, and leverages existing data of various forms (such as documents containing legislation and directives, structured data from government agencies' operational systems, social media data, etc.), in order to develop a new digital channel of communication between citizens and government. Making use of appropriately structured and semantically annotated data, this channel enables ‘richer’ and more expressive interaction of citizens with government in everyday language, facilitating and advancing both information seeking and conducting of transactions. Compared to existing digital channels, the proposed approach is appropriate for a wider range of citizens' interactions, with higher levels of complexity, ambiguity and uncertainty. In close co-operation with three Greek government agencies (the Ministry of Finance, a social security organization, and a big local government organization), this approach has been validated through a series of application scenarios.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.