A hierarchical Naïve Bayes model for approximate identity matching

G. Alan Wang, Homa Atabakhsh, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Organizations often manage identity information for their customers, vendors, and employees. Identity management is critical to various organizational practices ranging from customer relationship management to crime investigation. The task of searching for a specific identity is difficult because disparate identity information may exist due to the issues related to unintentional errors and intentional deception. In this paper we propose a hierarchical Naïve Bayes model that improves existing identity matching techniques in terms of searching effectiveness. Experiments show that our proposed model performs significantly better than the exact-match based matching technique. With 50% training instances labeled, the proposed semi-supervised learning achieves a performance comparable to the fully supervised record comparison algorithm. The semi-supervised learning greatly reduces the efforts of manually labeling training instances without significant performance degradation.

Original languageEnglish (US)
Pages (from-to)413-423
Number of pages11
JournalDecision Support Systems
Issue number3
StatePublished - Jun 2011


  • EM algorithm
  • Entity matching
  • Hierarchical Naïve Bayes model
  • Identity management
  • Semi-supervised learning

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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