J. Tang, H. fung Leung, Q. Luo, D. Chen, and J. Gong. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, page 2089-2094. Pasadena, California, USA, (2009)
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.
%0 Conference Paper
%1 paper:tang:2009
%A Tang, Jie
%A fung Leung, Ho
%A Luo, Qiong
%A Chen, Dewei
%A Gong, Jibin
%B Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence
%C Pasadena, California, USA
%D 2009
%K 2009 IJCAI folksonomy ontology
%P 2089-2094
%T Towards Ontology Learning from Folksonomies
%U http://ijcai.org/papers09/Papers/IJCAI09-344.pdf
%X 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.
@inproceedings{paper:tang:2009,
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.},
added-at = {2009-07-14T23:52:12.000+0200},
address = {Pasadena, California, USA},
author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
biburl = {https://www.bibsonomy.org/bibtex/20a1b7cd673bd2c30039acccd657fabc2/mschuber},
booktitle = {Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence},
interhash = {17f95a6ba585888cf45443926d8b7e98},
intrahash = {0a1b7cd673bd2c30039acccd657fabc2},
keywords = {2009 IJCAI folksonomy ontology},
pages = {2089-2094},
timestamp = {2009-07-14T23:52:12.000+0200},
title = {Towards Ontology Learning from Folksonomies},
url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
year = 2009
}