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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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). https://doi.org/10.3785/j.issn.1008-973X.2015.09.007
Dong LY, Wang YQ, He JN, et al., 2017. Collaborative fil-tering recommendation algorithm based on time decay. J Jilin Univ Eng Technol Ed, 47(4):1268–1272 (in Chinese). https://doi.org/10.13229/j.cnki.jdxbgxb201704036
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). https://doi.org/10.13196/j.cims.2013.07.82.dongsy.016
Fan ZP, Feng Y, Sun YH, et al., 2005. A framework on com-pound knowledge push system oriented to organizational employees. Proc 1st Int Workshop on Internet and Net-work Economics, p.622–630. https://doi.org/10.1007/11600930_62
Feng YX, Zhang SY, Gao YC, et al., 2016. Intelligent push method of CNC design knowledge based on feature se-mantic analysis. Comput Integr Manuf Syst, 22(1):189–201 (in Chinese). https://doi.org/10.13196/j.cims.2016.01.018
Friedman N, Geiger D, Goldszmidt M, 1997. Bayesian net-work classifiers. Mach Learn, 29(2-3):131–163. https://doi.org/10.1002/9780470400531.eorms0099
Goldberg D, Nichols D, Oki BM, et al., 1992. Using collabo-rative filtering to weave an information tapestry. Commun ACM, 35(12):61–70. https://doi.org/10.1145/138859.138867
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). https://doi.org/10.13196/j.cims.2013.01.9.jix.008
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). https://doi.org/10.3969/j.issn.1001-3997.2012.01.096
Jiang H, Yin P, Guo L, et al., 2017. Knowledge push based on design flow and user capacity model. MATEC Web Conf, Article 12. https://doi.org/10.1051/matecconf/201713900012
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. https://doi.org/10.1155/2015/495309
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 2078 626. https://doi.org/10.1155/2017/2078626
Liang Y, Zhang SY, Liu XJ, et al., 2015. Product design knowledge dynamic push technology based on variable-weight layered spreading activation model. Comput In-tegr Manuf Syst, 21(12):3107–3118 (in Chinese). https://doi.org/10.13196/j.cims.2015.12.002
Liu TY, Wang HF, He Y, 2016. Intelligent knowledge rec-ommending approach for new product development based on workflow context matching. Concurr Eng, 24(4): 318–329. https://doi.org/10.1177/1063293X16640319
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). https://doi.org/10.6041/j.issn.1000-1298.2012.01.035
Schreiber G, 2000. Knowledge Engineering and Management: the Common KADS Methodology. MIT Press, Cambridge, MA.
Shen MY, Qiu LM, Tan JR, et al., 2015. Active push design of product subdivision structure driven by performance re-quirement. J Zhejiang Univ Eng Sci, 49(2):287–295 (in Chinese). https://doi.org/10.3785/j.issn.1008-973X.2015.02.014
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. https://doi.org/10.5220/0005538002630270
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. https://doi.org/10.1049/cp.2009.1511
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). https://doi.org/10.3969/j.issn.1006-5911.2007.02.005
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. https://doi.org/10.1145/1242572.1242729
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. https://doi.org/10.1177/0954406215584395
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. https://doi.org/10.1145/1361684.1361686
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. https://doi.org/10.13196/j.cims.2016.05.002
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. https://doi.org/10.1016/j.jnca.2013.04.010
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 In-telligence, p.492–496. https://doi.org/10.1109/ISAI.2016.0111
Yoshii K, Goto M, Komatani K, et al., 2008. An efficient hybrid music recommender system using an incremen-tally trainable probabilistic generative model. IEEE Trans Audio Speech Lang Process, 16(2):435–447. https://doi.org/10.1109/TASL.2007.911503
Zhang FP, Li L, 2016. Research on knowledge push method for business process based on multidimensional hierar-chical context model. Proc IEEE Int Conf on Industrial Engineering and Engineering Management, p.656–660. https://doi.org/10.1109/IEEM.2016.7797957
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. https://doi.org/10.1109/ICEBEG.2011.5877024
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. https://doi.org/10.1109/ICNDS.2009.134
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Project supported by the National Natural Science Foundation of China (No. 51675478), the Natural Science Foundation of Zhejiang Province, China (No. LY15E050004), and Youth Funds of the State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University
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Zhang, Sy., Gu, Y., Liu, Xj. et al. A knowledge push technology based on applicable probability matching and multidimensional context driving. Frontiers Inf Technol Electronic Eng 19, 235–245 (2018). https://doi.org/10.1631/FITEE.1700763
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DOI: https://doi.org/10.1631/FITEE.1700763