Collaborative and Usage-Driven Evolution of Personal Ontologies

  • Peter Haase
  • Andreas Hotho
  • Lars Schmidt-Thieme
  • York Sure
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)


Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.


Recommender System Description Logic Domain Ontology Ontology Model Change Operation 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Peter Haase
    • 1
  • Andreas Hotho
    • 2
  • Lars Schmidt-Thieme
    • 3
  • York Sure
    • 1
  1. 1.Institute AIFBU of KarlsruheGermany
  2. 2.Knowledge Discovery Engineering GroupU of KasselGermany
  3. 3.Computer-based New Media Group, Institute for Computer ScienceU of FreiburgGermany

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