Applying semantic technologies to public sector: A case study in fraud detection

Abstract

Fraudulent claims cost both the public and private sectors an enormous amount of money each year. The existence of data silos is considered one of the main barriers to cross-region, cross-department, and cross-domain data analysis that can detect abnormalities not easily seen when focusing on single data sources. An evident advantage of lever-aging Linked Data and semantic technologies is the smooth integration of distributed data sets. This paper reports a proof-of-concept study in the benefit fraud detection area. We believe that the design considerations, study outcomes, and learnt lessons can help making decisions of how one should adopt semantic technologies in similar contexts. © Springer-Verlag 2013.