Towards the Design of an Advanced Knowledge-Based Portal for Enterprises: The KBMS 2.0 Project

  • Silvia Calegari
  • Matteo Dominoni
  • Emanuele Panzeri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8482)


This paper presents the KBMS 2.0 Project, a system that is aimed at managing and searching information effectively. The idea is to leverage the information stored in any enterprise to support users during their interactions with the defined portal. The advanced portal has been developed to help users in finding information related to their information needs with the use of a friendly user interface. This task has been done by considering two key aspects: (1) context, and (2) personalization, respectively. Contextual information allows to filter out knowledge not related to users, whereas personalization aspects have been used to support users during their searches by considering user preferences and user activities.


Personalization Aspect Friendly User Interface Navigation Module Protect Category Logical Division 
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.
    Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring user interest. IEEE Internet Computing 5, 32–39 (2001)CrossRefGoogle Scholar
  2. 2.
    Dey, A.K.: Understanding and using context. Pers Ubiquitous Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  3. 3.
    Dourish, P.: What we talk about when we talk about context. Personal and Ubiquitous Computing 8 (2004)Google Scholar
  4. 4.
    Harman, D.: Overview of the fourth text retrieval conference (trec-4). In: TREC (1995)Google Scholar
  5. 5.
    Ingwersen, P., Järvelin, K.: Information retrieval in context: Irix. SIGIR Forum 39, 31–39 (2005)CrossRefGoogle Scholar
  6. 6.
    Jansen, B.J., Spink, A.: An analysis of documents viewing patterns of web search engine users. In: Scime, A. (ed.) Web mining: Applications and techniques, pp. 339–354. IGI Publishing, Hershey (2004)CrossRefGoogle Scholar
  7. 7.
    Ma, Z., Pant, G., Sheng, O.R.L.: Interest-based personalized search. ACM Trans. Inf. Syst. 25(1) (2007)Google Scholar
  8. 8.
    Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: The Adaptive Web: Methods and Strategies of Web Personalization, ch. 6, pp. 195–230 (2007)Google Scholar
  9. 9.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 449–456. ACM, New York (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Silvia Calegari
    • 1
  • Matteo Dominoni
    • 1
  • Emanuele Panzeri
    • 1
  1. 1.Department of Informatics, Systems and Communication (DISCo)University of Milano-BicoccaMilanoItaly

Personalised recommendations