@inproceedings{auyeung2007tag,
  abstract = {Collaborative tagging systems are becoming very popular recently. Web users use freely-chosen tags to describe shared resources, resulting in a folksonomy. One problem of folksonomies is that tags which appear in the same form may carry multiple meanings and represent different concepts. As this kind of tags are ambiguous, the precisions in both description and retrieval of the shared resources are reduced. We attempt to develop effective methods to disambiguate tags by studying the tripartite structure of folksonomies. This paper describes the network analysis techniques that we employ to discover clusters of nodes in networks and the algorithm for tag disambiguation. Experiments show that the method is very effective in performing the task.},
  author = {man Au Yeung, Ching and Gibbins, Nicholas and Shadbolt, Nigel},
  bibdate = {2008-02-06},
  bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/iat/iatw2007.html#YeungGS07},
  booktitle = {Web Intelligence/IAT Workshops},
  crossref = {conf/iat/2007w},
  file = {auyeung2007tag.pdf:auyeung2007tag.pdf:PDF},
  groups = {public},
  interhash = {715a5b5e7a4d3dca918e89e9be7a77fb},
  intrahash = {a7e6e642d676b6ce9b5e5b3a4d350eac},
  pages = {3--6},
  publisher = {IEEE},
  timestamp = {2010-11-30 18:26:37},
  title = {Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies},
  url = {http://dx.doi.org/10.1109/WIIATW.2007.4427527},
  username = {dbenz},
  year = 2007
}

@inproceedings{si2009disambiguating,
  abstract = {Blog users enjoy tagging for better document organization, while ambiguity in tags leads to inaccuracy in tag-based applications, such as retrieval, visualization or trend discovery. The dynamic nature of tag meanings makes current word sense disambiguation(WSD) methods not applicable. In this paper, we propose an unsupervised method for disambiguating tags in blogs. We first cluster the tags by their context words using Spectral Clustering. Then we compare a tag with these clusters to find the most suitable meaning. We use Normalized Google Distance to measure word similarity, which can be computed by querying search engines, thus reflects the up-to-date meaning of words. No human labeling efforts or dictionary needed in our method. Evaluation using crawled blog data showed a promising micro average precision of 0.842.},
  author = {Si, Xiance and Sun, Maosong},
  bibdate = {2009-08-27},
  bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/tsd/tsd2009.html#SiS09},
  booktitle = {TSD},
  editor = {Matousek, V{\'a}clav and Mautner, Pavel},
  file = {si2009disambiguating.pdf:si2009disambiguating.pdf:PDF},
  groups = {public},
  interhash = {685c11a49e8ef69d6d41cef4b1db27c9},
  intrahash = {de61eca3fd7a58891a451f98bfc361ed},
  isbn = {978-3-642-04207-2},
  pages = {139--146},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2010-11-30 18:26:55},
  title = {Disambiguating Tags in Blogs},
  url = {http://dx.doi.org/10.1007/978-3-642-04208-9},
  username = {dbenz},
  volume = 5729,
  year = 2009
}