@inproceedings{lu2009exploit,
  abstract = {In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.},
  address = {New York, NY, USA},
  author = {Lu, Caimei and Chen, Xin and Park, E. K.},
  booktitle = {CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management},
  doi = {10.1145/1645953.1646167},
  interhash = {e192e53972f28d78f1ecbffbfea08bed},
  intrahash = {a120cece36e15b12321c87e7d0938d73},
  isbn = {978-1-60558-512-3},
  location = {Hong Kong, China},
  pages = {1545--1548},
  publisher = {ACM},
  title = {Exploit the tripartite network of social tagging for web clustering},
  url = {http://portal.acm.org/citation.cfm?id=1646167&dl=GUIDE&coll=GUIDE&CFID=93888742&CFTOKEN=72927742},
  year = 2009
}

@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.},
  address = {San Francisco, CA, USA},
  author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
  booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  year = 2009
}