Applying BPANN and hierarchical ontology to develop a methodology for binary knowledge document classification and content analysis

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

*Corresponding author for this work

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Nowadays many companies rely on patent engineers to search patent documents and offer recommendation and advice to R&D engineers. Given the great number of patent documents, new means to effectively and efficiently identify and manage the technology-specific patent documents are required. This research applies back-propagation artificial neural network (BPANN), a hierarchical ontology, and Normalized term frequency (NTF) method for binary document classification and content analysis. This approach helps to minimize inappropriate patent document classification. Hence, the approach reduces the effort to search and select patents for analysis. Finally, this paper use the design of exposure machines as a case study to illustrate and verify the efficacy of the approach proposed in this paper.

原文English
主出版物標題Proceedings of the 2010 14th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2010
頁面263-268
頁數6
DOIs
出版狀態Published - 1 十二月 2010
事件2010 14th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2010 - Shanghai, China
持續時間: 14 四月 201016 四月 2010

出版系列

名字Proceedings of the 2010 14th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2010

Conference

Conference2010 14th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2010
國家China
城市Shanghai
期間14/04/1016/04/10

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