Towards Ontology Learning from Folksonomies
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 2089-2093.
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.Jie Tang, Ho-fung Leung, Qiong Luo, Dewei Chen, Jibin Gong
@inproceedings{ijcai2009p344,
title = {Towards Ontology Learning from Folksonomies},
author = {Tang, Jie and Leung, Ho-fung and Luo, Qiong and Chen, Dewei and Gong, Jibin},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {2089--2093},
year = {2009},
url = {https://ijcai.org/proceedings/2009/344},
}
TY - CPAPER TI - Towards Ontology Learning from Folksonomies AU - Tang, Jie AU - Leung, Ho-fung AU - Luo, Qiong AU - Chen, Dewei AU - Gong, Jibin T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 2089 EP - 2093 UR - https://ijcai.org/proceedings/2009/344 ER -Copied
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