An Intelligent System for Automated Binary Knowledge Document Classification and Content Analysis
Tzu-An Chiang (National Taipei College of Business, Taiwan)
Chun-Yi Wu (National Tsing Hua University, Taiwan)
Charles V. Trappey (National Chiao Tung University, Taiwan)
Amy J. C. Trappey (National Tsing Hua University, Taiwan)
Abstract: Many companies rely on patent engineers to search patent documents and offer recommendations and advice to R and 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.
Keywords: BPANN, document classification, hierarchical ontology, normalized term frequency
Categories: H.3.1, H.3.3