Full Text:   <253>

Summary:  <152>

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: 809

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiao-jian Liu

http://orcid.org/0000-0001-8147-9954

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

10.1631/FITEE.1700763


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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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.

基于适用概率匹配与多维情境驱动的设计知识推送技术

概要:为了提高产品智能设计过程中设计知识的使用效率和质量,有必要向设计人员主动推送设计知识。知识推送主要包括知识匹配和匹配结果的合理推送两个方面。针对现有知识匹配通常缺乏智能性和匹配结果推送缺少个性化的问题,提出基于适用概率匹配和多维情境驱动的设计知识推送技术。构建包括设计知识表示向量、设计案例特征向量和映射布尔矩阵等的训练样本集,通过贝叶斯理论计算设计知识适用与不适用于设计内容的概率,即二者之间的匹配度,得到推送知识集。构建等级化设计内容模型对推送知识集进行过滤,通过设计知识、设计上下文、设计内容和设计人员等多维情境驱动,实现个性化的设计知识推送。在数控机床智能设计平台中的知识推送应用,证明了该技术的可行性和正确性。

关键词:产品设计;知识推送;适用概率匹配;多维情境;个性化

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Reference

[1]Chen S, Yang ZY, Sun LY, et al., 2015. Research on design knowledge analytical method during sketching-combining acoustic energy feature and creative segment theory. J Zhejiang Univ Eng Sci, 49(9):2073-2082 (in Chinese).

[2]Dong LY, Wang YQ, He JN, et al., 2017. Collaborative filtering recommendation algorithm based on time decay. J Jilin Univ Eng Technol Ed., 47(4):1268-1272 (in Chinese).

[3]Dong SY, Xu JX, Wang KQ, et al., 2013. Active push model of manufacturing process knowledge in CAD platform based on immune process. Comput Integr Manuf Syst, 19(7):1520-1531 (in Chinese).

[4]Fan ZP, Feng Y, Sun YH, et al., 2005. A framework on compound knowledge push system oriented to organizational employees. Proc 1st Int Workshop on Internet and Network Economics, p.622-630.

[5]Feng YX, Zhang SY, Gao YC, et al., 2016. Intelligent push method of CNC design knowledge based on feature semantic analysis. Comput Integr Manuf Syst, 22(1):189-201 (in Chinese).

[6]Friedman N, Geiger D, Goldszmidt M, 1997. Bayesian network classifiers. Mach Learn, 29(2-3):131-163.

[7]Goldberg D, Nichols D, Oki BM, et al., 1992. Using collaborative filtering to weave an information tapestry. Commun ACM, 35(12):61-70.

[8]Ji X, Gu XJ, Dai F, et al., 2013. Technology for product design knowledge push based on ontology and rough sets. Comput Integr Manuf Syst, 19(1):7-20 (in Chinese).

[9]Jiang CQ, Li BS, Lu WX, 2012. Research on knowledge push for mechanical product collaborative design based on case library. Mech Des Manuf, (1):257-259 (in Chinese).

[10]Jiang H, Yin P, Guo L, et al., 2017. Knowledge push based on design flow and user capacity model. MATEC Web Conf, Article 12.

[11]Li C, Li WQ, Li Y, et al., 2015. Research and application of knowledge resources network for product innovation. Sci World J, Article 495 309.

[12]Li XR, Yu SH, Chu JJ, et al., 2017. Double push strategy of knowledge for product design based on complex network theory. Discr Dynam Nat Soc, Article 2 078 626.

[13]Liang Y, Zhang SY, Liu XJ, et al., 2015. Product design knowledge dynamic push technology based on variable-weight layered spreading activation model. Comput Integr Manuf Syst, 21(12):3107-3118 (in Chinese).

[14]Liu TY, Wang HF, He Y, 2016. Intelligent knowledge recommending approach for new product development based on workflow context matching. Concurr Eng, 24(4): 318-329.

[15]Mao J, Cao NL, Cao YL, et al., 2012. Matching method for quality knowledge in product designing process. Trans Chin Soc Agric Mach, 43(1):197-201 (in Chinese).

[16]Schreiber G, 2000. Knowledge Engineering and Management: the Common KADS Methodology. MIT Press, Cambridge, MA.

[17]Shen MY, Qiu LM, Tan JR, et al., 2015. Active push design of product subdivision structure driven by performance requirement. J Zhejiang Univ Eng Sci, 49(2):287-295 (in Chinese).

[18]Wang FL, Liao WH, Guo Y, et al., 2015. Reduction and push technology of cable harness information for complex mechatronic products based on variable precision rough sets. Proc 5th Int Conf on Simulation and Modeling Methodologies, Technologies and Applications, p.263-270.

[19]Wang S, Yin GF, He ZX, 2009. Active push technology for multidisciplinary auxiliary knowledge in product design. Proc Int Conf on Technology and Innovation, p.1-5.

[20]Wang SF, Gu XJ, Guo JF, et al., 2007. Knowledge active push for product design. Comput Integr Manuf Syst, 13(2): 234-239 (in Chinese).

[21]Wang XJ, Qin Y, Liu W, 2007. A search-based Chinese word segmentation method. Proc 16th Int Conf on World Wide Web, p.1129-1130.

[22]Wang ZS, Tian L, Wu YH, et al., 2016. Personalized knowledge push system based on design intent and user interest. Proc Inst Mech Eng Part C J Mech Eng Sci, 230(11):1757-1772.

[23]Wu HC, Luk RWP, Wong KF, et al., 2008. Interpreting TF-IDF term weights as making relevance decisions. ACM Trans Inform Syst, 26(3), Article 13.

[24]Xu RZ, Gao Q, Wang H, et al., 2016. Product design knowledge recommendation based on sequential pattern mining. Comput Integr Manuf Syst, 22(5):1179-1186.

[25]Xu YH, Yin GF, Nie Y, et al., 2013. Research on an active knowledge push service based on collaborative intent capture. J Netw Comput Appl, 36(6):1418-1430.

[26]Yan Y, Yang N, Hao J, et al., 2016. A context modeling method of knowledge recommendation for designers. Proc Int Conf on Information System and Artificial Intelligence, p.492-496.

[27]Yoshii K, Goto M, Komatani K, et al., 2008. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Trans Audio Speech Lang Process, 16(2):435-447.

[28]Zhang FP, Li L, 2016. Research on knowledge push method for business process based on multidimensional hierarchical context model. Proc IEEE Int Conf on Industrial Engineering and Engineering Management, p.656-660.

[29]Zhi ZL, Yuan Y, Yan ZG, et al., 2011. Knowledge active push based on personalized interest model in aircraft structure design. Proc Int Conf on E-Business and E-Government, p.1-4.

[30]Zhou LZ, Liu DF, Wang B, et al., 2009. Research on knowledge active push model for product development. Proc Int Conf on Networking and Digital Society, p.217-220.

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