Towards ontology learning from folksonomies

Jie Tang*, Ho Fung Leung, Qiong Luo, Dewei Chen, Jibin Gong

*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/ReportConference Paper published in a bookpeer-review

75 Citations (Scopus)

Abstract

A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.

Original languageEnglish
Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2089-2094
Number of pages6
ISBN (Print)9781577354260
Publication statusPublished - 2009
Event21st International Joint Conference on Artificial Intelligence, IJCAI 2009 - Pasadena, United States
Duration: 11 Jul 200916 Jul 2009

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference21st International Joint Conference on Artificial Intelligence, IJCAI 2009
Country/TerritoryUnited States
CityPasadena
Period11/07/0916/07/09

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