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CLC number: TP391

On-line Access: 2018-04-09

Received: 2017-11-16

Revision Accepted: 2018-02-17

Crosschecked: 2018-02-19

Cited: 0

Clicked: 5417

Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-jian Liu


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.2 P.235-245


A knowledge push technology based on applicable probability matching and multidimensional context driving

Author(s):  Shu-you Zhang, Ye Gu, Xiao-jian Liu, Jian-rong Tan

Affiliation(s):  State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liuxj@zju.edu.cn

Key Words:  Product design, Knowledge push, Applicable probability matching, Multidimensional context, Personalization

Shu-you Zhang, Ye Gu, Xiao-jian Liu, Jian-rong Tan. A knowledge push technology based on applicable probability matching and multidimensional context driving[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(2): 235-245.

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T1 - A knowledge push technology based on applicable probability matching and multidimensional context driving
A1 - Shu-you Zhang
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A1 - Xiao-jian Liu
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J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700763

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push’, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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