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: 5829
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.
@article{title="A knowledge push technology based on applicable probability matching and multidimensional context driving",
author="Shu-you Zhang, Ye Gu, Xiao-jian Liu, Jian-rong Tan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="2",
pages="235-245",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700763"
}
%0 Journal Article
%T A knowledge push technology based on applicable probability matching and multidimensional context driving
%A Shu-you Zhang
%A Ye Gu
%A Xiao-jian Liu
%A Jian-rong Tan
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 2
%P 235-245
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700763
TY - JOUR
T1 - A knowledge push technology based on applicable probability matching and multidimensional context driving
A1 - Shu-you Zhang
A1 - Ye Gu
A1 - Xiao-jian Liu
A1 - Jian-rong Tan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 2
SP - 235
EP - 245
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700763
Abstract: 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.
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