TY - GEN
T1 - An exploratory study of navigating Wikipedia semantically
T2 - 4th International Conference on Online Communities and Social Computing, OCSC 2011, Held as Part of HCI International 2011
AU - Wu, I. Chin
AU - Lin, Yi Sheng
AU - Liu, Che Hung
N1 - Funding Information:
Acknowledgments. This research was supported by the National Science Council and Fu-Jen Catholic University of Taiwan under the Grant No. 99-2410-H-030-047-MY3 & No.409931074078, respectively.
PY - 2011
Y1 - 2011
N2 - Due to the popularity of link-based applications like Wikipedia, one of the most important issues in online research is how to alleviate information overload on the World Wide Web (WWW) and facilitate effective information-seeking. To address the problem, we propose a semantically-based navigation application that is based on the theories and techniques of link mining, semantic relatedness analysis and text summarization. Our goal is to develop an application that assists users in efficiently finding the related subtopics for a seed query and then quickly checking the content of articles. We establish a topic network by analyzing the internal links of Wikipedia and applying the Normalized Google Distance algorithm in order to quantify the strength of the semantic relationships between articles via key terms. To help users explore and read topic-related articles, we propose a SNA-based summarization approach to summarize articles. To visualize the topic network more efficiently, we develop a semantically-based WikiMap to help users navigate Wikipedia effectively.
AB - Due to the popularity of link-based applications like Wikipedia, one of the most important issues in online research is how to alleviate information overload on the World Wide Web (WWW) and facilitate effective information-seeking. To address the problem, we propose a semantically-based navigation application that is based on the theories and techniques of link mining, semantic relatedness analysis and text summarization. Our goal is to develop an application that assists users in efficiently finding the related subtopics for a seed query and then quickly checking the content of articles. We establish a topic network by analyzing the internal links of Wikipedia and applying the Normalized Google Distance algorithm in order to quantify the strength of the semantic relationships between articles via key terms. To help users explore and read topic-related articles, we propose a SNA-based summarization approach to summarize articles. To visualize the topic network more efficiently, we develop a semantically-based WikiMap to help users navigate Wikipedia effectively.
KW - Navigation
KW - Normalized Google Distance
KW - SNA-based summary
KW - Semantically-based
KW - Wikipedia
UR - http://www.scopus.com/inward/record.url?scp=79960318383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960318383&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21796-8_15
DO - 10.1007/978-3-642-21796-8_15
M3 - Conference contribution
AN - SCOPUS:79960318383
SN - 9783642217951
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 149
BT - Online Communities and Social Computing - 4th International Conference, OCSC 2011, Held as Part of HCI International 2011, Proceedings
Y2 - 9 July 2011 through 14 July 2011
ER -