@inproceedings{jaeschke2009testing,
  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.},
  address = {New York, NY, USA},
  author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
  booktitle = {RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems},
  interhash = {440fafda1eccf4036066f457eb6674a0},
  intrahash = {1320904b208d53bd5d49e751cbfcc268},
  location = {New York, NY, USA},
  note = {(to appear)},
  publisher = {ACM},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  year = 2009
}

@phdthesis{jaschke2011formal,
  address = {Heidelberg},
  author = {Jäschke, Robert},
  interhash = {dcb2cd1cd72ae45d77c4d8755d199405},
  intrahash = {bad02a0bbbf066907ecdee0ecaf9fb80},
  isbn = {1-60750-707-2},
  publisher = {Akad. Verl.-Ges. AKA},
  series = {Dissertations in artificial intelligence},
  title = {Formal concept analysis and tag recommendations in collaborative tagging systems},
  url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038},
  volume = 332,
  year = 2011
}

@inproceedings{lipczak2010impact,
  abstract = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -- folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor -- resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.},
  acmid = {1810648},
  address = {New York, NY, USA},
  author = {Lipczak, Marek and Milios, Evangelos},
  booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia},
  doi = {10.1145/1810617.1810648},
  interhash = {a999b5f2eace0cd75028e57261afe3d7},
  intrahash = {71dd1a473eaf0af9840758653746c221},
  isbn = {978-1-4503-0041-4},
  location = {Toronto, Ontario, Canada},
  numpages = {10},
  pages = {179--188},
  publisher = {ACM},
  series = {HT '10},
  title = {The Impact of Resource Title on Tags in Collaborative Tagging Systems},
  url = {http://doi.acm.org/10.1145/1810617.1810648},
  year = 2010
}

@phdthesis{jaschke2011formal,
  address = {Heidelberg},
  author = {Jäschke, Robert},
  interhash = {dcb2cd1cd72ae45d77c4d8755d199405},
  intrahash = {bad02a0bbbf066907ecdee0ecaf9fb80},
  isbn = {1-60750-707-2},
  publisher = {Akad. Verl.-Ges. AKA},
  series = {Dissertations in artificial intelligence},
  title = {Formal concept analysis and tag recommendations in collaborative tagging systems},
  url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038},
  volume = 332,
  year = 2011
}

@inproceedings{lorince2014supertagger,
  author = {Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter},
  interhash = {4af29810e9c882dc18f560527c65de2f},
  intrahash = {014abc7dc30e38859c5e8605dce1a8f6},
  title = {“Supertagger” Behavior in Building Folksonomies},
  year = 2014
}

@inproceedings{heckner2009personal,
  address = {San Jose, CA, USA},
  author = {Heckner, Markus and Heilemann, Michael and Wolff, Christian},
  booktitle = {Int'l AAAI Conference on Weblogs and Social Media (ICWSM)},
  interhash = {f954e699dc6ca2d0abbe5f6ebe166dc7},
  intrahash = {d1074484ea350ad88400fe4fc6984874},
  month = may,
  title = {Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems},
  year = 2009
}

@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
}

@inproceedings{cattuto2007vocabulary,
  abstract = { We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of local vocabularies, that is the growth of the number of distinct tags used in the context of a given resource or user. In both cases, we find power-law behaviors with exponents smaller than one. Surprisingly, the observed growth behaviors are remarkably regular throughout the entire history of the system and across very different resources being bookmarked. Similar sub-linear laws of growth have been observed in written text, and this qualitative universality calls for an explanation and points in the direction of non-trivial cognitive processes in the complex interaction patterns characterizing collaborative tagging.},
  author = {Cattuto, Ciro and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio},
  interhash = {7de017393b2d48335e209a9db23e08b6},
  intrahash = {fb163dd424fa1eb40640340f27ee0ea4},
  title = {Vocabulary growth in collaborative tagging systems},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0704.3316},
  year = 2007
}

@article{eda2009effectiveness,
  abstract = {In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like “cool�? and “fun�? from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy.We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags.},
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  file = {eda2009effectiveness.pdf:eda2009effectiveness.pdf:PDF},
  groups = {public},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  journalpub = {1},
  number = 4,
  pages = {421-440},
  timestamp = {2010-08-15 15:00:40},
  title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
  url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09},
  username = {dbenz},
  volume = 12,
  year = 2009
}

@inproceedings{mori2006extracting,
  abstract = {Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.},
  author = {Mori, Junichiro and Tsujishita, Takumi and Matsuo, Yutaka and Ishizuka, Mitsuru},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2006},
  ee = {http://dx.doi.org/10.1007/11926078_35},
  file = {mori2006extracting.pdf:mori2006extracting.pdf:PDF},
  groups = {public},
  interhash = {457973d894180bd95e99bb6f7bb5cbc5},
  intrahash = {f1a145a60c3e4d39e91b39a7c1178110},
  pages = {487-500},
  timestamp = {2009-06-01 15:32:20},
  title = {Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts},
  username = {dbenz},
  year = 2006
}

@inproceedings{tang2009towards,
  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},
  file = {tang2009towards.pdf:tang2009towards.pdf:PDF},
  groups = {public},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  timestamp = {2009-12-23 21:30:44},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  username = {dbenz},
  year = 2009
}

@inproceedings{wagner2010wisdom,
  abstract = {Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called \tweetonomy", together with a set of stream-based measures that allow researchers to systematically define and compare different stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical findings demonstrate that different social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.},
  author = {Wagner, C. and Strohmaier, M.},
  booktitle = {Proc. of the Semantic Search 2010 Workshop (SemSearch2010)},
  file = {wagner2010wisdom.pdf:wagner2010wisdom.pdf:PDF},
  groups = {public},
  interhash = {02c222a4f9abd5964ea61af034769af4},
  intrahash = {2f96232a648d4fd1617c389d899f3d2b},
  location = {Raleigh, NC, USA},
  month = {april},
  timestamp = {2010-04-19 08:03:47},
  title = {The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams},
  url = {http://mstrohm.wordpress.com/2010/04/17/on-taxonomies-folksonomies-and-tweetonomies/},
  username = {dbenz},
  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
}

@misc{Xu09relevanceranking,
  author = {Xu, Jun and Li, Hang and Zhong, Chaoliang},
  interhash = {c0a86e1785768ef1f15d5cacc1442597},
  intrahash = {4d086714a580d80c68077fcc98656db3},
  title = {Relevance Ranking using Kernels},
  url = {http://www.google.de/url?sa=t&source=web&cd=2&ved=0CCEQFjAB&url=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F81437%2FMSR_TechReport_2009_Kernel4IR.pdf&rct=j&q=Relevance%20Ranking%20using%20Kernels&ei=uzftTM28GMr2sgaO4Y35Dg&usg=AFQjCNFftCUJMs7LgoqEXR2VvT7bQ7FWHw&sig2=H5OBpauNrYXJ0asAFrEuGQ&cad=rja},
  year = 2009
}

@article{journals/www/EdaYUU09,
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  number = 4,
  pages = {421-440},
  title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
  url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09},
  volume = 12,
  year = 2009
}

@article{lerman2006social,
  author = {Lerman, K. and Jones, L.},
  interhash = {ca87d38a2278011eea5cd7faefd0f2df},
  intrahash = {31208c29afdd24b49b0dd290821b0fa0},
  journal = {Arxiv preprint cs/0612047},
  title = {{Social browsing on flickr}},
  url = {http://scholar.google.de/scholar.bib?q=info:4ZJ0zK6yr5wJ:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=0},
  year = 2006
}