@inproceedings{zhou2008unsupervised,
  abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.},
  author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong},
  file = {zhou2008unsupervised.pdf:zhou2008unsupervised.pdf:PDF},
  groups = {public},
  interhash = {e8397fd51d43531b91e81776c879f487},
  intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819},
  journal = {The Semantic Web},
  pages = {680--693},
  timestamp = {2009-09-24 23:27:32},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49},
  username = {dbenz},
  year = 2008
}

@article{zhou2008unsupervised,
  abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.},
  author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong},
  file = {zhou2008unsupervised.pdf:zhou2008unsupervised.pdf:PDF},
  interhash = {e8397fd51d43531b91e81776c879f487},
  intrahash = {ee6da1cc1300cf4fb68fc58d5e2bb819},
  journal = {The Semantic Web},
  pages = {680--693},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49},
  year = 2008
}

@inproceedings{Zhou/2007/Unsupervised,
  abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency.},
  address = {Berlin, Heidelberg},
  author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong},
  booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
  crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
  editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe},
  interhash = {af21595ee9f4a13b5e651ad049f31262},
  intrahash = {355fcbb32255f3ba5f41819c00c520ba},
  month = {November},
  pages = {673--686},
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations},
  url = {http://iswc2007.semanticweb.org/papers/673.pdf},
  volume = 4825,
  year = 2007
}

@inproceedings{Bao_2007,
  abstract = {This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding web pages; 2) the count of annotations indicates the popularity of web pages. Two novel algorithms are proposed to incorporate the above information into page ranking: 1) SocialSimRank (SSR) calculates the similarity between social annotations and web queries; 2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web users’ perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawled from del.icio.us. Experiments show that both SSR and SPR benefit web search significantly.},
  address = {New York, NY, USA},
  author = {Bao, Shenghua and Xue, Guirong and Wu, Xiaoyuan and Yu, Yong and Fei, Ben and Su, Zhong},
  booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web},
  interhash = {2cbdc7da88c90ef22468108c1f481159},
  intrahash = {b9966b9df0199a0b7b2d5a1b0d7560cb},
  privnote = {Models for search using social annotations: similarity ranking (tags as summaries of documents) and static ranking (number of annotations). Use delicious and ODP data.},
  publisher = {ACM Press},
  title = {Optimizing web search using social annotations},
  year = 2007
}