Alignment-Based Preprocessing of Personal Ontologies on Semantic Social Network

  • Jason J. Jung
  • Hong-Gee Kim
  • Geun-Sik Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)


In semantic social network, the relations between users are inferred by measuring the similarity between the corresponding personal ontologies. However, “over-enriched” personal ontologies have caused some difficulties in being discriminated from other personal ontologies. For efficiently annotating resources in their own repositories, people simply append ontology fragments retrieved from standard ontologies and from other neighbors’ personal ontologies along to social links. In this paper, we propose a preprocessing method to extract preferential concepts for comparing with social semantics. In order to prune out irrelevant concepts from personal ontologies, alignment-based concept classification process is designed by checking these two main criteria; i) redundancy (e.g., if there already exist semantically identical concepts), and ii) tendency (e.g., if there exist semantically declined concepts). Finally, we want to show an application scenario to demonstrate our contributions.


Social Network Analysis Preferential Weight Identical Concept Simple Aggregation Preferential Concept 
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 2007

Authors and Affiliations

  • Jason J. Jung
    • 1
  • Hong-Gee Kim
    • 2
  • Geun-Sik Jo
    • 1
  1. 1.Inha UniversityKorea
  2. 2.Seoul National UniversityKorea

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