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Towards ontology learning from folksonomies

by: Jie Tang, Ho fung Leung, Qiong Luo, Dewei Chen, and Jibin Gong

In: IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. (2009) , p. 2089--2094.

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URL:	http://ijcai.org/papers09/Papers/IJCAI09-344.pdf

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.

Description

Towards ontology learning from folksonomies

BibTeX record

@inproceedings{1661779,
  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 = {2009-12-23T18:06:56.000+0100},
  address = {San Francisco, CA, USA},
  author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
  biburl = {http://www.bibsonomy.org/bibtex/27b335f08a288a79eb70eff89f1ec7630/hotho},
  booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence},
  description = {Towards ontology learning from folksonomies},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  keywords = {folksonomy learning model ol tagging taggingsurvey topic toread},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  timestamp = {2009-12-23T18:06:56.000+0100},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  year = 2009
}

Endnote record

%0 Conference Paper
%1 1661779
%A Tang, Jie
%A fung Leung, Ho
%A Luo, Qiong
%A Chen, Dewei
%A Gong, Jibin
%B IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence
%C San Francisco, CA, USA
%D 2009
%I Morgan Kaufmann Publishers Inc.
%K 
%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.

BibSonomy :: publication ::

Towards ontology learning from folksonomies

Resources (URL, PDF, PS...)

Abstract

Description

BibTeX record

Endnote record