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Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts
Junichiro Mori1,2, Takumi Tsujishita1, Yutaka Matsuo2, and Mitsuru Ishizuka1
1University of Tokyo, Japan
jmori@mi.ci.i.u-tokyo.ac.jp
tjstkm@mi.ci.i.u-tokyo.ac.jp
ishizuka@mi.ci.i.u-tokyo.ac.jp
2National Institute of Advanced Industrial Science and Technology, Japan
y.matsuo@aist.go.jp
Abstract. 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.
LNCS 4273, p. 487 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2006