Show simple item record

dc.contributor.advisorChen, Hsinchunen_US
dc.contributor.authorWang, Gang Alan
dc.creatorWang, Gang Alanen_US
dc.date.accessioned2011-12-06T13:38:10Z
dc.date.available2011-12-06T13:38:10Z
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10150/195089
dc.description.abstractDue to the rapid development of information technologies, especially the network technologies, business activities have never been as integrated as they are now. Business decision making often requires gathering information from different sources. This dissertation focuses on the problem of entity matching, associating corresponding information elements within or across information systems. It is devoted to providing complete and accurate information for business decision making. Three challenges have been identified that may affect entity matching performance: feature selection for entity representative, matching techniques, and searching strategy. This dissertation first provides a theoretical foundation for entity matching by connecting entity matching to the similarity and categorization theories developed in the field of cognitive science. The theories provide guidance for tackling the three challenges identified. First, based on the feature contrast similarity model, we propose a case-study-based methodology that identifies key features that uniquely identify an entity. Second, we propose a record comparison technique and a multi-layer naïve Bayes model that correspond respectively to the deterministic and the probability response selection models defined in the categorization theory. Experiments show that both techniques are effective in linking deceptive criminal identities. However, the probabilistic matching technique is preferable because it uses a semi-supervised learning method, which requires less human intervention during training. Third, based on the prototype access assumption proposed in the categorization theory, we apply an adaptive detection algorithm to entity matching so that efficiency can be greatly improved by the reduced search space. Experiments show that this technique significantly improves matching efficiency without significant accuracy loss. Based on the above findings we developed the Arizona IDMatcher, an identity matching system based on the multi-layer naïve Bayes model and the adaptive detection method. We compare the proposed system against the IBM Identity Resolution tool, a leading commercial product developed using heuristic decision rules. Experiments do not suggest a clear winner, but provide the pros and cons of each system. The Arizona IDMatcher is able to capture more true matches than IBM Identity Resolution (i.e., high recall). On the other hand, the matches identified by IBM Identity Resolution are mostly true matches (i.e., high precision).
dc.language.isoENen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectentity matchingen_US
dc.subjectdeception detectionen_US
dc.subjectidentity matchingen_US
dc.titleEntity Matching for Intelligent Information Integrationen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.contributor.chairChen, Hsinchunen_US
dc.identifier.oclc752259906en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberNunamaker, Jay F.en_US
dc.contributor.committeememberZhang, Zhuen_US
dc.identifier.proquest1720en_US
thesis.degree.disciplineManagement Information Systemsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.nameDMgten_US
refterms.dateFOA2018-09-03T19:55:27Z
html.description.abstractDue to the rapid development of information technologies, especially the network technologies, business activities have never been as integrated as they are now. Business decision making often requires gathering information from different sources. This dissertation focuses on the problem of entity matching, associating corresponding information elements within or across information systems. It is devoted to providing complete and accurate information for business decision making. Three challenges have been identified that may affect entity matching performance: feature selection for entity representative, matching techniques, and searching strategy. This dissertation first provides a theoretical foundation for entity matching by connecting entity matching to the similarity and categorization theories developed in the field of cognitive science. The theories provide guidance for tackling the three challenges identified. First, based on the feature contrast similarity model, we propose a case-study-based methodology that identifies key features that uniquely identify an entity. Second, we propose a record comparison technique and a multi-layer naïve Bayes model that correspond respectively to the deterministic and the probability response selection models defined in the categorization theory. Experiments show that both techniques are effective in linking deceptive criminal identities. However, the probabilistic matching technique is preferable because it uses a semi-supervised learning method, which requires less human intervention during training. Third, based on the prototype access assumption proposed in the categorization theory, we apply an adaptive detection algorithm to entity matching so that efficiency can be greatly improved by the reduced search space. Experiments show that this technique significantly improves matching efficiency without significant accuracy loss. Based on the above findings we developed the Arizona IDMatcher, an identity matching system based on the multi-layer naïve Bayes model and the adaptive detection method. We compare the proposed system against the IBM Identity Resolution tool, a leading commercial product developed using heuristic decision rules. Experiments do not suggest a clear winner, but provide the pros and cons of each system. The Arizona IDMatcher is able to capture more true matches than IBM Identity Resolution (i.e., high recall). On the other hand, the matches identified by IBM Identity Resolution are mostly true matches (i.e., high precision).


Files in this item

Thumbnail
Name:
azu_etd_1720_sip1_m.pdf
Size:
971.4Kb
Format:
PDF
Description:
azu_etd_1720_sip1_m.pdf

This item appears in the following Collection(s)

Show simple item record