A hierarchical Naïve Bayes model for approximate identity matching

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• 作者：G. Alan Wang^a ; ^{alanwang@vt.edu"" rel=""nofollow} ; Homa Atabakhsh^b ; ¹ ; ^{homa@eller.arizona.edu"" rel=""nofollow} ; Hsinchun Chen^b ; ¹ ; ^{hchen@eller.arizona.edu"" rel=""nofollow} [Author vitae]
• 关键词：EM algorithm ; Entity matching ; Identity management ; Hierarchical Naï ; ve Bayes model ; Semi-supervised learning
• 刊名：Decision Support Systems
• 出版年：2011
• 出版时间：June 2011
• 年：2011
• 卷：51
• 期：3
• 页码：413-423
• 全文大小：1588 K

文摘

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.