An intelligent system for automated binary knowledge document classification and content analysis

Tzu An Chiang, Chun Yi Wu, Charles V. Trappey, Amy J.C. Trappey

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Many 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.

Original languageEnglish
Pages (from-to)1991-2008
Number of pages18
JournalJournal of Universal Computer Science
Volume17
Issue number14
DOIs
StatePublished - 2011

Keywords

  • BPANN
  • Document classification
  • Hierarchical ontology
  • Normalized term frequency

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