Analysis of the DBLP Publication Classification Using Concept Lattices

  • Saleh Alwahaishi
  • Jan Martinovič
  • Václav Snášel
Part of the Communications in Computer and Information Science book series (CCIS, volume 194)


The definitive classification of scientific journals depends on their aims and scopes details. In this paper, we present an approach to facilitate the journals classification of the DBLP datasets. For the analysis, the DBLP data sets were pre-processed by assigning each journal attributes defined by its topics and then the theory of formal concept analysis is introduced. It is subsequently shown how this theory can be applied to analyze the relations between journals and the extracted topics from their aims and scopes. The result is a concept lattice that contains information on journal-topic relational context depending on how they are associated. It is shown how this approach can be used to facilitate the classifications of scientific journals.


Recommender System Formal Concept Concept Lattice Nonnegative Matrix Factorization Formal Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht (1982)CrossRefGoogle Scholar
  2. 2.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical foundations. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  3. 3.
    Beydoun, G.: Using Formal Concept Analysis towards Cooperative E-Learning. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS (LNAI), vol. 5465, pp. 109–117. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Beydoun, G., Kultchitsky, R., Manasseh, G.: Evolving semantic web with social navigation. Expert Systems with Applications 32, 265–276 (2007)CrossRefGoogle Scholar
  5. 5.
    Maddouri, M.: A Formal Concept Analysis Approach to Discover Association Rules from Data. In: Belohlavek, R. (ed.) CLA, 10-21 (2005)Google Scholar
  6. 6.
    Priss, U.: Formal Concept Analysis in Information Science. In: Blaise, C. (ed.) Annual Review of Information Science and Technology. ASIST, vol. 40 (2006)Google Scholar
  7. 7.
    Ganter, B., Kuznetsov, S.O.: Scale Coarsening as Feature Selection. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 217–228. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Stumme, G., Bestride, Y., Taouil, R., Lakhal, L.: computing Iceberg Concept lattices with TITANIC. Data and Knowledge Engineering 42(2), 189–222 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Stumme, G., Wille, R., Wille, U.: Conceptual knowledge discovery in databases using Formal Concept Analysis Methods. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 450–458. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Ley, M.: The dblp computer science bibliography: Evolution, research issues, perspectives. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 1–10. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Zaiane, O.R., Chen, J., Goebel, R.: Dbconnect: mining research community on dblp data. In: WebKDD/SNA-KDD 2007: Proceedings of the 9th WebKDD and 1st SNA-KDD, Workshop on Web Mining and Social Network Analysis, pp. 74–81. ACM, New York (2007)Google Scholar
  13. 13.
    Klamma, R., Cuong, P.M., Cao, Y.: You never walk alone: Recommending academic events based on social network analysis. Complex (1), 657–670 (2009)Google Scholar
  14. 14.
    Chan, S., Pon, R., Cardenas, A.: Visualization and clustering of author social networks. In: Distributed Multimedia Systems Conference, pp. 174–180 (2006)Google Scholar
  15. 15.
    Peng, Y., Kou, G., Shi, Y.: Recent trends in data mining: Document clustering of dm publications. In: International Conference on Service Systems and Service Management, vol. 2, pp. 1653–1659 (2006)Google Scholar
  16. 16.
    Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: KDD 2006: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 374–383. ACM, New York (2006)Google Scholar
  17. 17.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: Rankclus: integrating clustering with ranking for heterogeneous information network analysis. In: EDBT 2009: Proceedings of the 12th International Conference on Extending Database Technology, pp. 565–576. ACM, New York (2009)Google Scholar
  18. 18.
    Li, T., Ding, C., Zhang, Y., Shao, B.: Knowledge transformation from word space to document space. In: SIGIR 2008: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 187–194. ACM, New York (2008)Google Scholar
  19. 19.
    Obadi, G., Drazdilova, P., Hlavacek, L., Martinovic, J., Snasel, V.: A Tolerance Rough Set Based Overlapping Clustering for the DBLP Data. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 57–60 (2010)Google Scholar
  20. 20.
    Krohn, U., Davies, N.J., Weeks, R.: Concept lattices for knowledge management. BT Technology Journal 17(4), 108–116 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Saleh Alwahaishi
    • 1
  • Jan Martinovič
    • 2
  • Václav Snášel
    • 2
  1. 1.Department of Accounting and MISKing Fahd University of Petroleum and MineralsSaudi Arabia
  2. 2.Department of Computer ScienceFEECS, VŠB- Technical University of OstravaCzech Republic

Personalised recommendations