Extracting keyphrases to represent relations in social networks from web

Junichiro Mori, Mitsuru Ishizuka, Yutaka Matsuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

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 with existing social network extraction methods. Our experiments conducted on entities in researcher social networks and political social networks achieved clustering with high precision and recall. The results showed that our method is able to extract appropriate relation labels to represent relations among entities in the social networks.

Original languageEnglish
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages2820-2825
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: 2007 Jan 62007 Jan 12

Other

Other20th International Joint Conference on Artificial Intelligence, IJCAI 2007
CountryIndia
CityHyderabad
Period07/1/607/1/12

ASJC Scopus subject areas

  • Artificial Intelligence

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