Multimedia Tools and Applications

, Volume 75, Issue 24, pp 17465–17486 | Cite as

A novel method for adaptive knowledge map construction in the aircraft development

  • Yanjie Lv
  • Gang Zhao
  • Yong Yu


Aircraft is a typical transportation facility and its development need to refer to the existing knowledge. With the rapid increase of knowledge, a knowledge map may deliver excess knowledge to users that they cannot manage at once, thereby causing the problem of knowledge overload. Hence, a novel method for adaptive knowledge map construction was proposed to solve this problem. First, the knowledge was semantically annotated and stored with the domain ontology and a knowledge model that integrates context. Then, user requirement was described by the context of product design, and knowledge nodes that met users’ requirement could be extracted from the knowledge retrieval technology on the basis of context similarity. Finally, the connection between knowledge nodes was constructed with a composite connection model, and the knowledge map was visualized using a hierarchical approach. To verify the effectiveness of the proposed method, the constructed knowledge map was applied in an airplane wing design to assist users in browsing the knowledge base. Results indicate that the proposed method can change the displayed contents according to user requirement and identify the displayed knowledge nodes at a highly acceptable level, the constructed knowledge map can guide users efficiently, and the knowledge overload can be reduced significantly.


Transportation facilities Aircraft Knowledge map Knowledge overload Domain ontology 



The research was supported by Chinese 863 - program - “the High Technology Research and Development Program”. The project number is 2009AA043302.


  1. 1.
    Afacan Y, Demirkan H (2011) An ontology-based universal design knowledge support system. Knowl-Based Syst 24(4):530–541CrossRefGoogle Scholar
  2. 2.
    Bergamaschi S, Guerra F, Leiba B (2010) Guest editors’ introduction: information overload. IEEE Internet Comput 14(6):10–13CrossRefGoogle Scholar
  3. 3.
    Chen TY (2008) Knowledge sharing in virtual enterprises via an ontology-based access control approach. Comput Ind 59(5):502–519CrossRefGoogle Scholar
  4. 4.
    Chiu DY, Pan YC (2014) Topic knowledge map and knowledge structure constructions with genetic algorithm, information retrieval, and multi-dimension scaling method. Knowl-Based Syst 67:412–428CrossRefGoogle Scholar
  5. 5.
    Hammiche S, Lopez B, Benbernou S, et al. (2007) Domain knowledge based queries for multimedia data retrieval. J Digit Inf Manag 5(2):75–81Google Scholar
  6. 6.
    Hao J, Yan Y, Gong L, et al. (2014) Knowledge map-based method for domain knowledge browsing. Decis Support Syst 61(5):106–114CrossRefGoogle Scholar
  7. 7.
    Hu MC, Sharif N, Baark E (2014) Information Technology Services: a key knowledge-intensive business service industry in Hong Kong SAR, China. Sci Technol Soc 19(1):27–55CrossRefGoogle Scholar
  8. 8.
    Jiang D, Xu Z, Chen Z, et al. (2011) Joint time–frequency sparse estimation of large-scale network traffic. Comput Netw 55(15):3533–3547CrossRefGoogle Scholar
  9. 9.
    Lee S, Yoon B, Park Y (2009) An approach to discovering new technology opportunities: keyword-based patent map approach. Technovation 29(6):481–497CrossRefGoogle Scholar
  10. 10.
    Lee C, Rahayu W, Nguyen UT (2014) Knowledge management technologies for semantic multimedia services. Multim Tools Appl 71(1):195–198CrossRefGoogle Scholar
  11. 11.
    Li H, Zhao N, Guan X (2009) Research on the design of knowledge map system in the product R&D department of the automobile industry. Libr Inf Sci 53(8):85–125Google Scholar
  12. 12.
    Li T, Zhou X, Wang K, et al (2015) A convergence of key-value storage systems from clouds to supercomputers. Concurr Comput Pract Experience 00:1–27Google Scholar
  13. 13.
    Lin FR, Hsueh CM (2006) Knowledge map creation and maintenance for virtual communities of practice. Inf Process Manag 42(2):551–568CrossRefGoogle Scholar
  14. 14.
    Lin Y, Yang J, Lv Z, et al. (2015) A self-assessment stereo capture model applicable to the internet of things. Sensors 15(8):20925–20944CrossRefGoogle Scholar
  15. 15.
    Lv Z, Tek A, Da Silva F, et al. (2013a) Game on, science-how video game technology may help biologists tackle visualization challenges. PloS One 8(3):57990CrossRefGoogle Scholar
  16. 16.
    Lv Y, Zhao G, Miao P, et al. (2013b) Construction of intelligence knowledge map for complex product development. J Eng Sci Technol Rev 6(3):82–87Google Scholar
  17. 17.
    Lv Z, Halawani A, Fen S, et al (2015) Touch-less interactive augmented reality game on vision based wearable device. Pers Ubiquit Comput 19(3):551–567Google Scholar
  18. 18.
    Nguyen SH, Chowdhury G (2013) Interpreting the knowledge map of digital library research (1990–2010). J Am Soc Inf Sci Technol 64(6):1235–1258CrossRefGoogle Scholar
  19. 19.
    Ong TH, Chen H, Sung W, et al. (2005) Newsmap: a knowledge map for online news. Decis Support Syst 39(4):583–597CrossRefGoogle Scholar
  20. 20.
    Shi MH, Wang T, Chen YD, et al. (2011) Knowledge push system based on business process and knowledge need. Comput Integr Manuf Syst 17(4):882–887Google Scholar
  21. 21.
    Su H, Jiang Z, Wu H (2005) Building knowledge map for product development. J Shanghai Jiaotong Univ 39(12):2034–2039Google Scholar
  22. 22.
    Sveen FO, Rich E, Jager M, et al. (2007) Overcoming organizational challenges to secure knowledge management. Inf Syst Front 9(5):481–492CrossRefGoogle Scholar
  23. 23.
    Tao TY, Ming Z (2012) An ontology-based information retrieval model for vegetables e-commerce. J Integr Agric 11(5):800–807CrossRefGoogle Scholar
  24. 24.
    Vail EF (1999) Knowledge mapping: getting started with knowledge management. Inf Syst Manag 16(4):10–23CrossRefGoogle Scholar
  25. 25.
    Wan S, Paris C, Dale R. “Supporting browsing-specific information needs: introducing the Citation-Sensitive In-Browser Summariser”. Web Semant Sci Serv Agents World Wide Web. 2010, 8(2):196–202Google Scholar
  26. 26.
    Wang K, Qiao K, Sadooghi I, et al (2015a) Load-balanced and locality-aware scheduling for data-intensive workloads at extreme scales. Concurr Comput Pract Experience 00:1–29Google Scholar
  27. 27.
    Wang Y, Su Y, Agrawal G (2015b) A novel approach for approximate aggregations over arrays. Proceedings of the 27th International Conference on Scientific and Statistical Database Management, ACM, Vol. 4Google Scholar
  28. 28.
    Wang JJY, Huang JZ, Sun Y, et al. (2015c) Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization. Expert Syst Appl 42(3):1278–1286CrossRefGoogle Scholar
  29. 29.
    Watthananon J, Mingkhwan A (2012) Optimizing knowledge management using knowledge map. Procedia Eng 32:1169–1177CrossRefGoogle Scholar
  30. 30.
    Wexler MN (1997) The who, what and why of knowledge mapping. J Knowl Manag 5(3):249–264CrossRefGoogle Scholar
  31. 31.
    Woo JH, Clayton MJ, Johnson RE, et al. (2004) Dynamic knowledge map: reusing experts’ tacit knowledge in the AEC industry. Autom Constr 13(2):203–207CrossRefGoogle Scholar
  32. 32.
    Xu Y, Bernard A (2011) Quantifying the value of knowledge within the context of product development. Knowl-Based Syst 24(1):166–175CrossRefGoogle Scholar
  33. 33.
    Yang C, Liu Z, Wang H, et al. (2013) Reusing design knowledge based on design cases and knowledge map. Int J Technol Des Educ 23(4):1063–1077CrossRefGoogle Scholar
  34. 34.
    Yoon B, Lee S, Lee G (2010) Development and application of a keyword-based knowledge map for effective R&D planning. Scientometrics 85(3):803–820MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zhang S, Yang C, Liu Z (2011) Applied research on hierarchical design structure matrix in product modular design. Modular Mach Tool Autom Manuf Tech 1:18–25CrossRefGoogle Scholar
  36. 36.
    Zhang X, Hou X, Chen X, et al. (2013) Ontology-based semantic retrieval for engineering domain knowledge. Neurocomputing 116:382–391CrossRefGoogle Scholar
  37. 37.
    Zhang S, Zhang X, Ou X (2014) After we knew it: empirical study and modeling of cost-effectiveness of exploiting prevalent known vulnerabilities across iaas cloud. Proceedings of the 9th ACM symposium on Information, computer and communications security, ACM, pp. 317–328Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina

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