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dc.contributor.authorChen, Hsinchun
dc.contributor.authorKim, Jinwoo, 1963-
dc.date.accessioned2004-09-04T00:00:01Z
dc.date.available2010-06-18T23:27:13Z
dc.date.issued1994-12en_US
dc.date.submitted2004-09-04en_US
dc.identifier.citationGANNET: A machine learning approach to document retrieval 1994-12, 11(3):9-43 Journal of Management Information Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/105547
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractInformation science researchers have recently turned to new artificial intelligence-based inductive learning techniques including neural networks, symbolic learning and genetic algorithms. An overview of the new techniques and their usage in information science research is provided. The algorithms adopted for a hybrid genetic algorithms and neural nets based system, called GANNET, are presented. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, the experiment showed that GANNET improved the Jaccard's scores by about 50% and helped identify the underlying concepts that best describe the user-selected documents.
dc.format.mimetypetext/htmlen_US
dc.language.isoenen_US
dc.publisherM.E. Sharpe, Inc.en_US
dc.subjectDatabase Searching Instructionsen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherGANNETen_US
dc.titleGANNET: A machine learning approach to document retrievalen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Management Information Systemsen_US
html.description.abstractInformation science researchers have recently turned to new artificial intelligence-based inductive learning techniques including neural networks, symbolic learning and genetic algorithms. An overview of the new techniques and their usage in information science research is provided. The algorithms adopted for a hybrid genetic algorithms and neural nets based system, called GANNET, are presented. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, the experiment showed that GANNET improved the Jaccard's scores by about 50% and helped identify the underlying concepts that best describe the user-selected documents.


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