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dc.contributor.authorTrappey, Amy J. C.en_US
dc.contributor.authorTrappey, Charles V.en_US
dc.contributor.authorChiang, Tzu-Anen_US
dc.contributor.authorHuang, Yi-Hsuanen_US
dc.date.accessioned2014-12-08T15:30:42Z-
dc.date.available2014-12-08T15:30:42Z-
dc.date.issued2013-04-01en_US
dc.identifier.issn0020-7543en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00207543.2012.701775en_US
dc.identifier.urihttp://hdl.handle.net/11536/21933-
dc.description.abstractIn order to stimulate innovation during the collaborative process of new product and production development, especially to avoid duplicating existing techniques or infringing upon others' patents and intellectual property rights, the collaborative team of research and development, and patent engineers must accurately identify relevant patent knowledge in a timely manner. This research develops a novel knowledge management approach using ontology-based artificial neural network (ANN) algorithm to automatically classify and search knowledge documents stored in huge online patent corpuses. This research focuses on developing a smart and semantic oriented classification and search from the sources of the most critical and well-structured knowledge publications, i.e. patents, to gain valuable and practical references for the collaborative networks of technology-centric product and production development teams. The research uses the domain ontology schema created using Protege and derives the semantic concept probabilities of key phrases that frequently occur in domain relevant patent documents. Then, by combining the term frequencies and the concept probabilities of key phrases as the ANN inputs, the method shows significant improvement in classification accuracy. In addition, this research provides an advanced semantic-oriented search algorithm to accurately identify related patent documents in the patent knowledge base. The case demonstration analyses 343 chemical mechanical polishing and 150 radio-frequency identification patents sample sets to verify and measure the performance of the proposed approach. The results are compared with the previous automatic classification methods demonstrating much improved outcomes.en_US
dc.language.isoen_USen_US
dc.titleOntology-based neural network for patent knowledge management in design collaborationen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/00207543.2012.701775en_US
dc.identifier.journalINTERNATIONAL JOURNAL OF PRODUCTION RESEARCHen_US
dc.citation.volume51en_US
dc.citation.issue7en_US
dc.citation.spage1992en_US
dc.citation.epage2005en_US
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000328322700005-
dc.citation.woscount3-
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