Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts

  • Junichiro Mori
  • Takumi Tsujishita
  • Yutaka Matsuo
  • Mitsuru Ishizuka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4273)


Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.


Social Network Social Network Analysis Context Model Relation Extraction Ontology Learning 
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 2006

Authors and Affiliations

  • Junichiro Mori
    • 1
    • 2
  • Takumi Tsujishita
    • 1
  • Yutaka Matsuo
    • 2
  • Mitsuru Ishizuka
    • 1
  1. 1.University of TokyoJapan
  2. 2.National Institute of Advanced Industrial Science and TechnologyJapan

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