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

Fingerprint

Recommender systems

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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
Markson, Christopher ; Song, Min. / A technique for suggesting related Wikipedia articles using link analysis. JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries. 2012. pp. 345-346
@inproceedings{d179dc36db8d495f8f7438067729750f,
title = "A technique for suggesting related Wikipedia articles using link analysis",
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.",
author = "Christopher Markson and Min Song",
year = "2012",
month = "7",
day = "11",
doi = "10.1145/2232817.2232883",
language = "English",
isbn = "9781450311540",
pages = "345--346",
booktitle = "JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries",

}

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, 12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12, Washington, DC, United States, 12/6/10. https://doi.org/10.1145/2232817.2232883

A technique for suggesting related Wikipedia articles using link analysis. / Markson, Christopher; Song, Min.

JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries. 2012. p. 345-346.

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

TY - GEN

T1 - A technique for suggesting related Wikipedia articles using link analysis

AU - Markson, Christopher

AU - Song, Min

PY - 2012/7/11

Y1 - 2012/7/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84863538698&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863538698&partnerID=8YFLogxK

U2 - 10.1145/2232817.2232883

DO - 10.1145/2232817.2232883

M3 - Conference contribution

AN - SCOPUS:84863538698

SN - 9781450311540

SP - 345

EP - 346

BT - JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries

ER -

Markson C, Song M. 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. 2012. p. 345-346 https://doi.org/10.1145/2232817.2232883