Developers have built many eGovernment applications using local ontologies to provide a metadata description of what their service does, how it works, and how to invoke it. Every day more ontologies are written by different developers and posted on web servers around the world. Consequently, effective bridging of semantic web applications for eGovernment is challenging. This paper briefly describes a new technique for bridging disjoint semantic web applications by automatically aligning their ontologies and performing message translation. The challenge is to create a tool to increase efficiency of alignment without reducing accuracy. We demonstrate our solution in an eGovernment scenario of translating driver's license information between two services in two U.S. states.
This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately.
In this paper, we introduce FIT, a STREP Project sponsored by the European Union starting in 2006. The overall objective of FIT is to develop, test and validate a selfadaptive e-government framework based on semantic technologies that will ensure that the quality of public services is proactively and continually fitted to the changing preferences and increasing expectations of e-citizens. We give a brief overview on the FIT scenario and indicate where and how we plan to utilize Semantic Web technologies.