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dc.contributor.authorChiang, Tzu-Anen_US
dc.contributor.authorWu, Chun-Yien_US
dc.contributor.authorTrappey, Charles V.en_US
dc.contributor.authorTrappey, Amy J. C.en_US
dc.date.accessioned2014-12-08T15:23:23Z-
dc.date.available2014-12-08T15:23:23Z-
dc.date.issued2011en_US
dc.identifier.issn0948-695Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/16371-
dc.description.abstractMany companies rely on patent engineers to search patent documents and offer recommendations and advice to R&D engineers. Given the increasing number of patent documents filed each year, new means to effectively and efficiently identify and manage technology specific patent documents are required. This research applies a back-propagation artificial neural network (BPANN), a hierarchical ontology technique, and a normalized term frequency (NTF) method to develop an intelligent system for binary knowledge document classification and content analysis. The intelligent system minimizes inappropriate patent document classification and reduces the effort required to search and screen patents for analysis. Finally, this paper uses the design of light emitting diode (LED) lamps as a case study to illustrate and verify the efficiency of automated binary knowledge document classification and content analysis.en_US
dc.language.isoen_USen_US
dc.subjectBPANNen_US
dc.subjectdocument classificationen_US
dc.subjecthierarchical ontologyen_US
dc.subjectnormalized term frequencyen_US
dc.titleAn Intelligent System for Automated Binary Knowledge Document Classification and Content Analysisen_US
dc.typeArticleen_US
dc.identifier.journalJOURNAL OF UNIVERSAL COMPUTER SCIENCEen_US
dc.citation.volume17en_US
dc.citation.issue14en_US
dc.citation.epage1991en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000304607900007-
dc.citation.woscount2-
Appears in Collections:Articles