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dc.contributor.authorXi, Wensien_US
dc.date.accessioned2014-03-14T20:13:36Z
dc.date.available2014-03-14T20:13:36Z
dc.date.issued2006-06-20en_US
dc.identifier.otheretd-06292006-202356en_US
dc.identifier.urihttp://hdl.handle.net/10919/28158
dc.description.abstractIn this dissertation I use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects and their inter-relationships. I argue that integrated and iterative computations over the Unified Relationship Matrix can help overcome the data sparseness problem (a common situation in various information application scenarios), and detect latent relationships (such as latent term associations discovered by LSI) among heterogeneous data objects. Thus, this kind of computation can be used to improve the quality of various information applications that require combining information from heterogeneous data sources. To support the argument, I further develop a unified link analysis algorithm, the Link Fusion algorithm, and a unified similarity-calculating algorithm, the SimFusion algorithm. Both algorithms attempt to better integrate information from heterogeneous sources by iteratively computing over the Unified Relationship Matrix in order to calculate some specific property of data object(s); such as the importance of a data object (as in the Link Fusion algorithm) and the similarity between a pair of data objects (as in the SimFusion algorithm). Then, I develop two set of experiments on real-world datasets to investigate whether the algorithms proposed in this dissertation can better integrate information from multiple sources. The performance of the algorithms is compared to that of traditional link analysis and similarity-calculating algorithms. Experimental results show that the algorithms developed can significantly outperform the traditional link analysis and similarity-calculating algorithms. I further investigate various pruning technologies aiming at improving efficiency and investigating the scalability of the algorithms designed. Experimental results showed that pruning technology can effectively be used to improve the efficiency of the algorithms.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartDissertationWensiXi.pdfen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectUnified Relationship Matrixen_US
dc.subjectInformation Retrievalen_US
dc.subjectInformation Integrationen_US
dc.subjectSimFusionen_US
dc.subjectSearchen_US
dc.subjectLink Fusionen_US
dc.titleIterative Computing over a Unified Relationship Matrix for Information Integrationen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairFan, Weiguo Patricken_US
dc.contributor.committeememberRamakrishnan, Narenen_US
dc.contributor.committeememberPonte, Jay M.en_US
dc.contributor.committeememberLu, Chang-Tienen_US
dc.contributor.committeememberSandu, Adrianen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06292006-202356/en_US
dc.date.sdate2006-06-29en_US
dc.date.rdate2009-09-06
dc.date.adate2006-09-06en_US


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