Identity matching based on probabilistic relational models

Jiexun Li, Gang Wang, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Identity management is critical to various organizational practices ranging from citizen services to crime investigation. The task of searching for a specific identity is difficult because multiple identity representations may exist due to issues related to unintentional errors and intentional deception. In this study we propose a probabilistic relational model (PRM) based approach to match identities in databases. By exploring a database relational structure, we derive three categories of features, namely personal identity features, social activity features, and social relationship features. Based on these derived features, a probabilistic prediction model can be constructed to make a matching decision on a pair of identities. An experimental study using a real criminal dataset demonstrates the effectiveness of the proposed PRM-based approach. By incorporating social activity features, the average precision of identity matching increased from 53.73 % to 54.64%; furthermore, the incorporation of social relation features increased the average precision to 68.27%.

Original languageEnglish (US)
Title of host publicationAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Pages1457-1464
Number of pages8
StatePublished - Dec 1 2006
Event12th Americas Conference on Information Systems, AMCIS 2006 - Acapulco, Mexico
Duration: Aug 4 2006Aug 6 2006

Publication series

NameAssociation for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006
Volume3

Other

Other12th Americas Conference on Information Systems, AMCIS 2006
CountryMexico
CityAcapulco
Period8/4/068/6/06

Keywords

  • Feature construction
  • Identity matching
  • Probabilistic relational models

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Library and Information Sciences
  • Information Systems

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  • Cite this

    Li, J., Wang, G., & Chen, H. (2006). Identity matching based on probabilistic relational models. In Association for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006 (pp. 1457-1464). (Association for Information Systems - 12th Americas Conference On Information Systems, AMCIS 2006; Vol. 3).