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Applying Semantic Technologies to Public Sector: A Case Study in Fraud Detection

  • Bo Hu
  • Nuno Carvalho
  • Loredana Laera
  • Vivian Lee
  • Takahide Matsutsuka
  • Roger Menday
  • Aisha Naseer
Conference paper
  • 1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7774)

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 leveraging 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.

Keywords

Fraud Detection Semantic Technology Single Data Source Original Data Source Public Sector Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bo Hu
    • 1
  • Nuno Carvalho
    • 1
  • Loredana Laera
    • 1
  • Vivian Lee
    • 1
  • Takahide Matsutsuka
    • 1
  • Roger Menday
    • 1
  • Aisha Naseer
    • 1
  1. 1.Fujitsu Laboratories Europe, Ltd.UK

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