A technique for suggesting related Wikipedia articles using link analysis

Christopher Markson, Min Song

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

Abstract

With more than 3.7 million articles, Wikipedia has become an important social medium for sharing knowledge. However, with this enormous repository of information, it can often be difficult to locate fundamental topics that support lower-level articles. By exploiting the information stored in the links between articles, we propose that related companion articles can be automatically generated to help further the reader's understanding of a given topic. This approach to a recommendation system uses tested link analysis techniques to present users with a clear path to related high-level articles, furthering the understanding of low-level topics.

Original languageEnglish
Title of host publicationJCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries
Pages345-346
Number of pages2
DOIs
Publication statusPublished - 2012 Jul 11
Event12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12 - Washington, DC, United States
Duration: 2012 Jun 102012 Jun 14

Other

Other12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12
CountryUnited States
CityWashington, DC
Period12/6/1012/6/14

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Markson, C., & Song, M. (2012). A technique for suggesting related Wikipedia articles using link analysis. In JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 345-346) https://doi.org/10.1145/2232817.2232883