Advertisement

Utilizing Mind-Maps for Information Retrieval and User Modelling

  • Joeran Beel
  • Stefan Langer
  • Marcel Genzmehr
  • Bela Gipp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8538)

Abstract

Mind-maps have been widely neglected by the information retrieval (IR) community. However, there are an estimated two million active mind-map users, who create 5 million mind-maps every year, of which a total of 300,000 is publicly available. We believe this to be a rich source for information retrieval applications, and present eight ideas on how mind-maps could be utilized by them. For instance, mind-maps could be utilized to generate user models for recommender systems or expert search, or to calculate relatedness of web-pages that are linked in mind-maps. We evaluated the feasibility of the eight ideas, based on estimates of the number of available mind-maps, an analysis of the content of mind-maps, and an evaluation of the users’ acceptance of the ideas. We concluded that user modelling is the most promising application with respect to mind-maps. A user modelling prototype – a recommender system for the users of our mind-mapping software Docear – was implemented, and evaluated. Depending on the applied user modelling approaches, the effectiveness, i.e. click-through rate on recommendations, varied between 0.28% and 6.24%. This indicates that mind-map based user modelling is promising, but not trivial, and that further research is required to increase effectiveness.

Keywords

mind-maps content analysis user modelling information retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Google: Ads in Gmail and your personal data (2012), https://support.google.com/mail/answer/6603
  2. 2.
    Zubiaga, A., Martinez, R., Fresno, V.: Getting the most out of social annotations for web page classification. In: Proceedings of the 9th ACM Symposium on Document Engineering, pp. 74–83 (2009)Google Scholar
  3. 3.
    Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for FW Lancaster. Library Trends 56, 784–815 (2008)CrossRefGoogle Scholar
  4. 4.
    Beel, J., Langer, S., Genzmehr, M., Nürnberger, A.: Introducing Docear’s Research Paper Recommender System. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013, pp. 459–460. ACM (2013)Google Scholar
  5. 5.
    Beel, J., Gipp, B., Langer, S., Genzmehr, M.: Docear: An Academic Literature Suite for Searching, Organizing and Creating Academic Literature. In: Proceedings of the 11th International ACM/IEEE Conference on Digital Libraries, pp. 465–466. ACM (2011)Google Scholar
  6. 6.
    Nesbit, J.C., Adesope, O.O.: Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research 76, 413 (2006)CrossRefGoogle Scholar
  7. 7.
    Brucks, C., Schommer, C.: Assembling Actor-based Mind-Maps from Text Stream. arXiv preprint (abs/0810.4616) (2008). Google Scholar
  8. 8.
    Mahler, T., Weber, M.: Dimian-Direct Manipulation and Interaction in Pen Based Mind Mapping. In: Proceedings of the 17th World Congress on Ergonomics, IEA 2009 (2009)Google Scholar
  9. 9.
    Beel, J., Langer, S.: An Exploratory Analysis of Mind Maps. In: Proceedings of the 11th ACM Symposium on Document Engineering, DocEng 2011, pp. 81–84. ACM (2011)Google Scholar
  10. 10.
    Beel, J., Gipp, B., Stiller, J.-O.: Information Retrieval on Mind Maps - What could it be good for? In: Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2009, pp. 1–4 (2009)Google Scholar
  11. 11.
    Gipp, B., Beel, J.: Citation Proximity Analysis (CPA) - A new approach for identifying related work based on Co-Citation Analysis. In: Proceedings of the 12th International Conference on Scientometrics and Informetrics ISSI 2009, pp. 571–575. International Society for Scientometrics and Informetrics, Rio de Janeiro (2009)Google Scholar
  12. 12.
    Chi, Y., Tseng, B.L., Tatemura, J.: Eigen-trend: trend analysis in the blogosphere based on singular value decompositions. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 68–77. ACM (2006)Google Scholar
  13. 13.
    Beel, J., Gipp, B., Mueller, C.: SciPlore MindMapping’ - A Tool for Creating Mind Maps Combined with PDF and Reference Management. D-Lib Magazine 15 (2009)Google Scholar
  14. 14.
  15. 15.
    Kiwitobes: Lessons on recommendation systems. Blog (2011), http://blog.kiwitobes.com/?p=58
  16. 16.
    Rich, E.: User modeling via stereotypes. Cognitive science 3, 329–354 (1979)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joeran Beel
    • 1
    • 2
  • Stefan Langer
    • 1
    • 2
  • Marcel Genzmehr
    • 1
  • Bela Gipp
    • 1
    • 3
  1. 1.DocearMagdeburgGermany
  2. 2.Otto-von-Guericke UniversityMagdeburgGermany
  3. 3.University of CaliforniaBerkeleyUSA

Personalised recommendations