An integrated approach for knowledge management in the context of product innovation



Companies use social media to communicate customers has been increasing recently. By analyzing the content generated by users online, companies obtain the information about market and use in product management and innovation, improving the competitiveness of enterprises. As the emerging of growing user-generated content (UGC), how to achieve effective information extraction and transform into product knowledge has posed a challenge to the enterprise. In the context of smart phone product innovation, with data fetched from websites and provided by the enterprise, by using natural language processing and semantic Web tools, this paper proposes an integrated method of innovation knowledge management based on UGC, which provide a systematic solution for the interactive innovation knowledge management.


Knowledge management Product innovation User-generated content Integrated approach 



The research is supported by National Natural Science Foundation of People’s Republic of China (71672074).


  1. 1.
    Alan Wang, G., Liu, X., Wang, J., Zhang, M., Fan, W.: Examining micro-level knowledge sharing discussions in online communities. Inf. Syst. Front. 17, 1227–1238 (2015). CrossRefGoogle Scholar
  2. 2.
    Lee, M.K.O., Cheung, C.M.K., Lim, K.H., Sia, C.L.: Understanding customer knowledge sharing in web-based discussion boards: an exploratory study. Internet Res. 16, 289–303 (2006)CrossRefGoogle Scholar
  3. 3.
    Wang, G.A., Jiao, J., Abrahams, A.S., Fan, W., Zhang, Z.: Expert rank: a topic-aware expert finding algorithm for online knowledge communities. Decis. Support Syst. 54(3), 1442–1451 (2013). CrossRefGoogle Scholar
  4. 4.
    Fan, W., Gordon, M.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014). CrossRefGoogle Scholar
  5. 5.
    Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M., Watts, D.J.: Predicting consumer behaviour with web search. Proc. Natl Acad. Sci. USA 107, 17486–17490 (2010)CrossRefGoogle Scholar
  6. 6.
    Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches (2013, April).
  7. 7.
  8. 8.
    Brar, G., Rossi, G.D., Kalamkar, N.: Predicting stock returns using text mining tools. In: Mitra, G., Yu, X. (eds.) Handbook of Sentiment Analysis in Finance, pp. 214–227. Optirisk Systems, Chennai (2016)Google Scholar
  9. 9.
    Abrahams, A.S., Fan, W., Wang, G.A., Zhang, Z., Jiao, J.: An integrated text analytic framework for product defect discovery. Prod. Oper. Manag. 24(6), 975–990 (2015). CrossRefGoogle Scholar
  10. 10.
    Jiang, M., Cui, P., Wang, F., Zhu, W., Yang, S.: Scalable recommendation with social contextual information. IEEE Trans. Knowl. Data Eng. 26(11), 2789–2802 (2014). CrossRefGoogle Scholar
  11. 11.
    Jin, X., Zhou, Z., Lee, M., Cheung, C.: Why users keep answering questions in online question answering communities: a theoretical and empirical investigation. Int. J. Inf. Manag. 33(1), 93–104 (2012). CrossRefGoogle Scholar
  12. 12.
    Spangler, S., Kreulen, J.: Mining the Talk: Unlocking the Business Value in Unstructured Information. IBM Press, Indianapolis (2008)Google Scholar
  13. 13.
    Wang, G., Liu, X., Fan, W.: A knowledge adoption model based framework for finding helpful user-generated contents in online communities. In: Proceedings of 30th Second International Conference on Information Systems, Shanghai, China (2011)Google Scholar
  14. 14.
    Yassine, A.A., Bradley, J.A.: A knowledge-driven, network-based computational framework for product development systems. J. Comput. Inf. Sci. Eng. 13(1), 65–83 (2013). CrossRefGoogle Scholar
  15. 15.
    Li, X., Tian, Y., Smarandache, F., et al.: An extension collaborative innovation model in the context of big data. Int. J. Inf. Technol. Decis. Mak. 14(1), 1–23 (2014). Google Scholar
  16. 16.
    Smith, F., Storti, E., Taglino, F.: Towards semantic collective awareness platforms for business innovation. In: Advanced Information Systems Engineering Workshops, pp. 226–237 (2014).
  17. 17.
    Wu, J., Guo, B., Shi, Y.: Customer knowledge management and IT-enabled business model innovation: a conceptual framework and a case study from China. Eur. Manag. J. 31(4), 359–372 (2013). CrossRefGoogle Scholar
  18. 18.
    Martinez-Torres, M.R.: Content analysis of open innovation communities using latent semantic indexing. Technol. Anal. Strateg. Manag. 27(1), 1–17 (2015). CrossRefGoogle Scholar
  19. 19.
    Chen, T., Shao, Y., Han, Y.: Collaborative Innovation Model Research Based on Knowledge-Supernetwork and TRIZ, pp. 1169–1174. Springer, Berlin (2015).
  20. 20.
    Lezcano, L., Sicilia, M.A., Rodríguez-Solano, C.: Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules. J. Biomed. Inform. 44(2), 343–353 (2011). CrossRefGoogle Scholar
  21. 21.
    Feigenbaum, L.: Semantic Web Technologies in the Enterprise (2006).
  22. 22.
    Latif, A., Hofler, P., Stocker, A., Saeed, A., Wagner, C.: The linked data value chain: a lightweight model for business engineers. In: Proceedings of International Conference on Semantic Systems, 2009Google Scholar
  23. 23.
    Li, C., Xie, T., Tang, Y.: GMVN oriented S-BOX knowledge expression and reasoning framework. J. Intell. Manuf. 25(5), 993–1011 (2014). CrossRefGoogle Scholar
  24. 24.
    Li, C., Xie, T., Tang, Y., Cao, C.: Integration methods based on the multidisciplinary semantic bill of X for the manufacturing demand caused by emergencies. Comput. Integr. Manuf. Syst. 21(4), 1063–1076 (2015)Google Scholar
  25. 25.
    Li, C., Xie, T., Tang, Y.: Knowledge expression and service on demand process reasoning framework of manufacturing system based on SWRL. Comput. Integr. Manuf. Syst. 19(1), 187–198 (2013)Google Scholar
  26. 26.
  27. 27.
    Hyland, B.: Preparing for a linked data enterprise. In: Wood, D. (ed.) Linking Enterprise Data, pp. 51–64. Springer, New York (2010)CrossRefGoogle Scholar
  28. 28.
    Graube, M, Ziegler, J., Urbas, L., Hladik, J.: Linked data as enabler for mobile applications for complex tasks in industrial settings. In: Emerging Technologies and Factory Automation, pp. 1–8 (2013).

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ManagementJinan UniversityGuangzhouPeople’s Republic of China
  2. 2.School of ManagementGuangzhou UniversityGuangzhouPeople’s Republic of China

Personalised recommendations