TY - GEN
T1 - A technique for suggesting related Wikipedia articles using link analysis
AU - Markson, Christopher
AU - Song, Min
PY - 2012
Y1 - 2012
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
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 345
EP - 346
BT - JCDL '12 - Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries
T2 - 12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12
Y2 - 10 June 2012 through 14 June 2012
ER -