Acquisition and Representation of Knowledge for Academic Field

  • Jie YuEmail author
  • Haiqiao Wu
  • Chao Tao
  • Lingyu Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9865)


With the rapid development of Internet, a large number of academic resources are available for researchers. How to organize these resources and represent them well is a big challenge. This paper proposes a novel method of acquiring and representing academic knowledge with cognitive characteristic and provides base for personalized academic services. In this paper, hierarchical knowledge for a specific research field is built according to the features of academic papers. Phrase is regarded as the center and clustering technique is applied with the neighboring condition of a phrase. In addition, semantic relationship between words and phrases is obtained. The experimental results illustrate the effectiveness of the method.


Cognitive Academic field Semantic relationship Hierarchical knowledge representation 


  1. 1.
    Meng, X.F., Ci, X.: Big data management: concepts, techniques and challenges. Comput. Res. Dev. 50(1), 146–169 (2013)Google Scholar
  2. 2.
    White, M.E.: What information explosion. Can. Vet. J. La Rev. Vet. Can. 30(8), 626–628 (1989)Google Scholar
  3. 3.
    Yang, L., Yuqing, S.: Search engine on academic knowledge map research. Inf. Knowl. (6), 105–110 (2010)Google Scholar
  4. 4.
    Guohe, F., Xiaoting, L.: Analysis of knowledge map of domestic recommendation engine in academic research. Inf. Sci. (1) (2012)Google Scholar
  5. 5.
    Xiaomei, H.: Knowledge map analysis of the present situation of library management in our country. Library (6), 114–117 (2011)Google Scholar
  6. 6.
    Feicheng, M., Juncheng, W., Yutao, Z.: Drawing of knowledge map of domestic life cycle theory – Based on the strategic map and conceptual network analysis. Inf. Sci. (4), 481–487 (2010)Google Scholar
  7. 7.
    Kim, S., Suh, E., Hwang, H.: Building the knowledge map: an industrial case study. J. Knowl. Manag. 7(2), 34–45 (2003)CrossRefGoogle Scholar
  8. 8.
    Zhang, H., Ling, T.: Application of bibliometric in the research of subject hot spots. In: The Thirteenth National Conference on Medical Informatics of the Chinese Medical Association (2007)Google Scholar
  9. 9.
    Nguyen, L.A.: Proposal of discovering user interest by support vector machine and decision tree on document classification. In: International Conference on Computational Science and Engineering, pp. 809–812 (2009)Google Scholar
  10. 10.
    Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 60(5), 493–502 (1972)CrossRefGoogle Scholar
  11. 11.
    Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behav. Res. Methods 28(2), 203–208 (1996)CrossRefGoogle Scholar
  12. 12.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)CrossRefzbMATHGoogle Scholar
  13. 13.
    Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (2013)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiPeople’s Republic of China

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