@article{10.1109/TKDE.2012.115,
  address = {Los Alamitos, CA, USA},
  author = {Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P.},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115},
  interhash = {f2e961e2b99fec0634b0d4fa3e001282},
  intrahash = {8a25332bfeb33e2ad8e1e1a062976da2},
  issn = {1041-4347},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  number = {PrePrints},
  publisher = {IEEE Computer Society},
  title = {Harnessing Folksonomies to Produce a Social Classification of Resources},
  volume = 99,
  year = 2012
}

@article{Zhang20125759,
  abstract = {Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.},
  author = {Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming},
  doi = {10.1016/j.physa.2012.05.013},
  interhash = {088ad59c786579d399aaee48db5e6a7a},
  intrahash = {84f824839090a5e20394b85a9e1cef08},
  issn = {0378-4371},
  journal = {Physica A: Statistical Mechanics and its Applications},
  number = 22,
  pages = {5759 - 5768},
  title = {Combining content and relation analysis for recommendation in social tagging systems},
  url = {http://www.sciencedirect.com/science/article/pii/S0378437112003846},
  volume = 391,
  year = 2012
}

@inproceedings{Laniado2010,
  author = {Laniado, David and Mika, Peter},
  booktitle = {International Semantic Web Conference (1)},
  crossref = {conf/semweb/2010-1},
  editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte},
  ee = {http://dx.doi.org/10.1007/978-3-642-17746-0_30},
  interhash = {3a63f88e11f958d548fa91fe442e1dcf},
  intrahash = {58dace4881efbd12c81ef1cc2e6bf7b9},
  isbn = {978-3-642-17745-3},
  pages = {470-485},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Making Sense of Twitter.},
  url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10},
  volume = 6496,
  year = 2010
}

@inproceedings{rezel2010swefe,
  abstract = {This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.},
  author = {Rezel, R. and Liang, S.},
  booktitle = {2010 International Symposium on Collaborative Technologies and Systems (CTS)},
  doi = {10.1109/CTS.2010.5478494},
  interhash = {9eb696593932c517873232386f8f61bf},
  intrahash = {d5b71572c7fea6504a0c0a3d84a9ecf0},
  month = may,
  pages = {349--356},
  publisher = {IEEE},
  title = {SWE-FE: Extending folksonomies to the Sensor Web},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494},
  year = 2010
}

@inproceedings{Kim2008,
  address = {Berlin, Deutschland},
  author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee},
  booktitle = {{Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}},
  interhash = {9c5f5af6f47a1a563dbb405c5a58a3cc},
  intrahash = {7d3c3c2189394a8686ca9812d58bfe74},
  pages = {128--137},
  publisher = {{Dublin Core Metadata Initiative}},
  title = {{The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}},
  year = 2008
}