@incollection{niebler2013tagging,
  abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which },
  author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas},
  booktitle = {Advances in Information Retrieval},
  doi = {10.1007/978-3-642-36973-5_8},
  editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine},
  interhash = {8f11f2140d9eb369a7ca42cd527f76c1},
  intrahash = {8583743a7598e78cc7b4e8af71a43902},
  isbn = {978-3-642-36972-8},
  pages = {86-97},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-36973-5_8},
  volume = 7814,
  year = 2013
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{doerfel2013analysis,
  abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.},
  acmid = {2507222},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert},
  booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
  doi = {10.1145/2507157.2507222},
  interhash = {3eaf2beb1cdad39b7c5735a82c3338dd},
  intrahash = {aa4b3d79a362d7415aaa77625b590dfa},
  isbn = {978-1-4503-2409-0},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {343--346},
  publisher = {ACM},
  series = {RecSys '13},
  title = {An analysis of tag-recommender evaluation procedures},
  url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
  year = 2013
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@article{journals/insk/KrauseLHRS12,
  author = {Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd},
  ee = {http://dx.doi.org/10.1007/s00287-010-0485-8},
  interhash = {3fca17b13ee1c002f41d3a2a4594b3e2},
  intrahash = {df97de393d3421ef2e20384ddde16ab1},
  journal = {Informatik Spektrum},
  number = 1,
  pages = {12-23},
  title = {Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.},
  url = {http://dblp.uni-trier.de/db/journals/insk/insk35.html#KrauseLHRS12},
  volume = 35,
  year = 2012
}

@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {64bf590675a833770b7d284871435a8d},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  month = sep,
  pages = {9--16},
  publisher = {ACM},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation },
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}

@inproceedings{benz2011measuring,
  abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.},
  address = {Heraklion, Crete},
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)},
  editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {67b4cd173ae1f6d98d80561b5f0289a4},
  month = may,
  title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf},
  year = 2011
}

@incollection{tagging-cattuto,
  abstract = {{Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Even though most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptions on the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity in terms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures of tag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding is provided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measures of semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of the investigated similarity measures and indicates which ones are better suited in the context of a given semantic application.}},
  author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {The Semantic Web - ISWC 2008},
  citeulike-article-id = {4718854},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-88564-1\_39},
  citeulike-linkout-1 = {http://www.springerlink.com/content/9044260283881v78},
  doi = {10.1007/978-3-540-88564-1\_39},
  editor = {Sheth, Amit and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy and Thirunarayan, Krishnaprasad},
  interhash = {b44538648cfd476d6c94e30bc6626c86},
  intrahash = {022ccb7184fcd0e43092fca13fd56a00},
  journal = {The Semantic Web - ISWC 2008},
  pages = {615--631},
  posted-at = {2011-09-09 20:06:23},
  priority = {2},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {{Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}},
  url = {http://tagora-project.eu/wp-content/2009/09/cattuto_iswc2008.pdf},
  volume = 5318,
  year = 2008
}

@incollection{jaeschke2012challenges,
  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}

@incollection{marinho2011social,
  abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the  noise  that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.},
  address = {New York},
  author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  doi = {10.1007/978-0-387-85820-3_19},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {708be7b5c269bd3a9d3d2334f858d52d},
  isbn = {978-0-387-85820-3},
  pages = {615--644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  year = 2011
}

@inproceedings{koerner2010thinking,
  abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence  of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.},
  address = {Raleigh, NC, USA},
  author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)},
  interhash = {5afe6e4ce8357d8ac9698060fb438468},
  intrahash = {45f8d8f2a8251a5e988c596a5ebb3f2d},
  month = apr,
  publisher = {ACM},
  title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity},
  url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf},
  year = 2010
}

@article{hotho2010publikationsmanagement,
  abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\"o}{\ss}erer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\"a}rtigkeit, die st{\"a}ndige Verf{\"u}gbarkeit, aber auch die M{\"o}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\"u}nde f{\"u}r ihren gegenw{\"a}rtigen Erfolg. Der Artikel f{\"u}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\"a}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie{\ss}t mit Querbez{\"u}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.},
  author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and J{\"a}schke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd},
  file = {dpunkt Product page:http\://hmd.dpunkt.de/271/05.html:URL},
  interhash = {4555775b639fe1ec65a302a61ee6532c},
  intrahash = {250d83c41fb10b89c73f54bd7040bd6e},
  issn = {1436-3011},
  journal = {HMD -- Praxis der Wirtschaftsinformatik},
  month = {#feb#},
  pages = {47-58},
  title = {{Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{\"u}r Wissenschaftler}},
  volume = {Heft 271},
  year = 2010
}

@techreport{GH05structure,
  author = {Golder, Scott and Huberman, Bernardo A.},
  citeulike-article-id = {305755},
  eprint = {cs.DL/0508082},
  institution = {Information Dynamics Lab, HP Labs    },
  interhash = {2d312240f16eba52c5d73332bc868b95},
  intrahash = {50f762bd270eeda14f71474e3c38795b},
  month = aug,
  priority = {2},
  title = {The Structure of Collaborative Tagging Systems},
  url = {http://arxiv.org/abs/cs.DL/0508082},
  year = 2005
}

@misc{citeulike:484851,
  abstract = {We describe online collaborative communities by tripartite networks,
    the nodes being persons, items and tags. We introduce projection
    methods in order to uncover the structures of the networks, i.e.
    communities of users, genre families... <br />To do so, we focus
    on the correlations between the nodes, depending on their profiles,
    and use percolation techniques that consist in removing less correlated
    links and observing the shaping of disconnected islands. The structuring
    of the network is visualised by using a tree representation. The
    notion of diversity in the system is also discussed.},
  author = {Lambiotte, R. and Ausloos, M.},
  citeulike-article-id = {484851},
  comment = {Paper about the three parts USERS, RESOURCES and TAGS.},
  eprint = {cs.DS/0512090},
  interhash = {7a9dab1c733e8e1982d5f91979749ce9},
  intrahash = {65c6f348a54f872fb3e60b4bd64b485b},
  month = dec,
  note = {{\tt arXiv:cs.DS/0512090}},
  priority = {3},
  title = {Collaborative tagging as a tripartite network},
  url = {http://arxiv.org/abs/cs.DS/0512090},
  year = 2005
}

@inproceedings{markines2009evaluating,
  abstract = {Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.},
  author = {Markines, Benjamin and Cattuto, Ciro and Menczer, Filippo and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {18th International World Wide Web Conference},
  interhash = {a266558ad4d83d536a0be2ac94b6b7df},
  intrahash = {d16e752a8295d5dad7e26b199d9f614f},
  month = {April},
  pages = {641--650},
  title = {Evaluating Similarity Measures for Emergent Semantics of Social Tagging},
  url = {http://www2009.eprints.org/65/},
  year = 2009
}

@inproceedings{grahl2007clustering,
  abstract = {Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the
system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.},
  address = {Graz, Austria},
  author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd},
  booktitle = {7th International Conference on Knowledge Management (I-KNOW '07)},
  interhash = {5cf58d2fdd3c17f0b0c54ce098ff5b60},
  intrahash = {334d3ab11400c4a3ea3ed5b1e95c1855},
  issn = {0948-695x},
  month = sep,
  pages = {356-364},
  publisher = {Know-Center},
  title = {Conceptual Clustering of Social Bookmarking Sites},
  year = 2007
}

@inproceedings{cattuto08-semantic,
  abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.},
  address = {Patras, Greece},
  author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)},
  interhash = {cc62b733f6e0402db966d6dbf1b7711f},
  intrahash = {3b0aca61b24e4343bd80390614e3066e},
  month = {July},
  title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems},
  url = {http://olp.dfki.de/olp3/},
  year = 2008
}

@inproceedings{grahl07conceptualKdml,
  author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)},
  editor = {Hinneburg, Alexander},
  interhash = {9c3bb05456bf11bcd88a1135de51f7d9},
  intrahash = {6d5188d66564fe4ed7386e28868504de},
  isbn = {978-3-86010-907-6},
  month = sep,
  pages = {50-54},
  publisher = {Martin-Luther-Universität Halle-Wittenberg},
  title = {Conceptual Clustering of Social Bookmark Sites},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf},
  vgwort = {14},
  year = 2007
}

@inproceedings{cattuto2008semantic,
  abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks
like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.},
  address = {Heidelberg},
  author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  booktitle = {The Semantic Web -- ISWC 2008, Proc.Intl. Semantic Web Conference 2008},
  editor = {Sheth, Amit P. and Staab, Steffen and Dean, Mike and Paolucci, Massimo and Maynard, Diana and Finin, Timothy W. and Thirunarayan, Krishnaprasad},
  interhash = {b44538648cfd476d6c94e30bc6626c86},
  intrahash = {27198c985b3bdb6daab0f7e961b370a9},
  pages = {615--631},
  publisher = {Springer},
  series = {LNAI},
  title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems},
  url = {http://dx.doi.org/10.1007/978-3-540-88564-1_39},
  volume = 5318,
  year = 2008
}

@inproceedings{Mcdonald01testingthe,
  abstract = {Distributional information has recently been implicated as playing an important role in several aspects of language ability. Learning the meaning of a word is thought to be dependent, at least in part, on exposure to the word in its linguistic contexts of use. In two experiments, we manipulated subjects ’ contextual experience with marginally familiar and nonce words. Results showed that similarity judgements involving these words were affected by the distributional properties of the contexts in which they were read. The accrual of contextual experience was simulated in a semantic space model, by successively adding larger amounts of experience in the form of item-in-context exemplars sampled from the British National Corpus. The experiments and the simulation},
  author = {Mcdonald, Scott and Ramscar, Michael},
  booktitle = {In Proceedings of the 23rd Annual Conference of the Cognitive Science Society},
  interhash = {e52a63b9e68235aa2596fe5f4443f642},
  intrahash = {dab2fb694c41ae1145195776d857368d},
  pages = {611--6},
  title = {Testing the distributional hypothesis: The influence of context on judgements of semantic similarity},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.7535},
  year = 2001
}

@inproceedings{conf/wsdm/AntonellisGK09,
  author = {Antonellis, Ioannis and Garcia-Molina, Hector and Karim, Jawed},
  booktitle = {WSDM (Late Breaking-Results)},
  crossref = {conf/wsdm/2009},
  date = {2009-03-12},
  editor = {Baeza-Yates, Ricardo A. and Boldi, Paolo and Ribeiro-Neto, Berthier A. and Cambazoglu, Berkant Barla},
  ee = {http://www.wsdm2009.org/wsdm2009_antonellis.pdf},
  interhash = {d7009d789ebc4efe38749a1078a06086},
  intrahash = {70539954a20f7d03a1f21764ff62c0ff},
  isbn = {978-1-60558-390-7},
  publisher = {ACM},
  title = {Tagging with Queries: How and Why?},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2009.html#AntonellisGK09},
  year = 2009
}

@inproceedings{jaeschke2009testingKDML,
  abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on 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 an evaluation of two exemplarily deployed recommendation methods, demonstrating
the power of the framework.},
  author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Workshop on Knowledge Discovery, Data Mining, and Machine Learning},
  editor = {Benz, Dominik and Janssen, Frederik},
  interhash = {440fafda1eccf4036066f457eb6674a0},
  intrahash = {5e8f40e610e723e966676772aa205f80},
  month = sep,
  pages = {44 --51},
  title = {Testing and Evaluating Tag Recommenders in a Live System},
  url = {http://lwa09.informatik.tu-darmstadt.de/pub/KDML/WebHome/kdml09_R.Jaeschke_et_al.pdf},
  year = 2009
}

@inproceedings{citeulike:965334,
  address = {New York, NY, USA},
  author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al M. and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, Maxwell F. and Riedl, John},
  booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work},
  citeulike-article-id = {965334},
  doi = {10.1145/1180875.1180904},
  interhash = {4f52a489eb6a696353d083b4d81b1fed},
  intrahash = {25f189ea6ed05c1df70cd58a053b9798},
  isbn = {1595932496},
  pages = {181--190},
  posted-at = {2009-04-01 05:03:08},
  priority = {4},
  publisher = {ACM Press},
  title = {tagging, communities, vocabulary, evolution},
  url = {http://dx.doi.org/10.1145/1180875.1180904},
  year = 2006
}

@inproceedings{Koutrika2007,
  author = {Koutrika, Georgia and Effendi, Frans Adjie and Gy\"{o}ngyi, Zolt\'{a}n and Heymann, Paul and Garcia-Molina, Hector},
  booktitle = {AIRWeb '07: Proc. of the 3rd int. workshop on Adversarial inf. retrieval on the web},
  interhash = {8b6de1f035a46f5465f1ed868a18c79a},
  intrahash = {899758f0e2b090bd3d6c24093ff7b94e},
  pages = {57--64},
  title = {Combating spam in tagging systems},
  year = 2007
}

@inproceedings{paper:lambiotte:2006,
  abstract = {We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.},
  author = {Lambiotte, Renaud and Ausloos, Marcel},
  booktitle = {Computational Science – ICCS 2006},
  interhash = {1ed3cd51137e724355234662fac3bdda},
  intrahash = {71f0fcb3b9b1cbe601da92fd3bf7ce60},
  pages = {1114-1117},
  publisher = {Springer Berlin / Heidelberg},
  title = {Collaborative Tagging as a Tripartite Network},
  year = 2006
}

@inproceedings{Schlitter2009,
  author = {Schlitter, Nico and Falkowski, Tanja},
  booktitle = {Proceedings of the International Conference on Advances in Social Network Analysis and Mining},
  interhash = {93f2e5bde1d859d917077fc63e702979},
  intrahash = {a796874b00de6a571f95d2e436f2828c},
  note = {to be published at The 2009 International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  title = {Mining the Dynamics of Music Preferences from a Social Networking Site},
  year = 2009
}

@inproceedings{jaeschke06trias,
  address = {Hong Kong},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd},
  booktitle = {Proceedings of the 6th IEEE International Conference on Data Mining     (ICDM 06)},
  doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162},
  interhash = {b4964c3bdd2991a80873d7080ef6a73e},
  intrahash = {e387c294129e11f4221514d5fa807e26},
  isbn = {0-7695-2701-9},
  issn = {1550-4786},
  month = {December},
  pages = {907-911},
  publisher = {IEEE Computer Society},
  title = {TRIAS - An Algorithm for Mining Iceberg Tri-Lattices},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf},
  vgwort = {19},
  year = 2006
}

@inproceedings{hotho2006entstehen,
  abstract = {Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.},
  address = {Baden-Baden},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Social Software in der Wertschöpfung},
  interhash = {1b39e4a77cac919f9030601711aad543},
  intrahash = {a333df6fdc7ff9322e3ce03988a7965e},
  publisher = {Nomos},
  title = {Das Entstehen von Semantik in BibSonomy},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006entstehen.pdf},
  year = 2006
}

@misc{Cattuto2006,
  abstract = {Collaborative tagging has been quickly gaining ground because of its
    ability to recruit the activity of web users into effectively organizing
    and sharing vast amounts of information. Here we collect data from
    a popular system and investigate the statistical properties of tag
    co-occurrence. We introduce a stochastic model of user behavior embodying
    two main aspects of collaborative tagging: (i) a frequency-bias mechanism
    related to the idea that users are exposed to each other's tagging
    activity; (ii) a notion of memory - or aging of resources - in the
    form of a heavy-tailed access to the past state of the system. Remarkably,
    our simple modeling is able to account quantitatively for the observed
    experimental features, with a surprisingly high accuracy. This points
    in the direction of a universal behavior of users, who - despite
    the complexity of their own cognitive processes and the uncoordinated
    and selfish nature of their tagging activity - appear to follow simple
    activity patterns.},
  author = {Cattuto, Ciro and Loreto, Vittorio and Pietronero, Luciano},
  citeulike-article-id = {695889},
  eprint = {cs/0605015},
  interhash = {59b1bd0ed96f41d2c3c98ff232df5dd2},
  intrahash = {8d265ea13915a79ec08fe13b8e7074c7},
  month = May,
  note = {{\tt arXiv:cs.CY/0605015}},
  priority = {3},
  title = {Collaborative Tagging and Semiotic Dynamics},
  url = {http://arxiv.org/abs/cs/0605015},
  year = 2006
}

@inproceedings{dkmnrt06visualizing,
  author = {Dubinko, M. and Kumar, R. and Magnani, J. and Novak, J. and Raghavan, P. and Tomkins, A.},
  booktitle = {Proc. of the 15th International WWW Conference},
  day = {23-25},
  interhash = {b9ff2f72831a1406013a86c8202d6276},
  intrahash = {e14f92577c8819bfd9753a047c6a8cea},
  title = {Visualizing Tags over Time},
  year = 2006
}

@inproceedings{1180904,
  abstract = {A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.},
  address = {New York, NY, USA},
  author = {Sen, Shilad and Lam, Shyong K. and Rashid, Al Mamunur and Cosley, Dan and Frankowski, Dan and Osterhouse, Jeremy and Harper, F. Maxwell and Riedl, John},
  booktitle = {CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work},
  doi = {http://doi.acm.org/10.1145/1180875.1180904},
  interhash = {96b20bffcbc91e528461529935524b90},
  intrahash = {582641c05e7a0b9396945a951822c83f},
  isbn = {1-59593-249-6},
  location = {Banff, Alberta, Canada},
  pages = {181--190},
  publisher = {ACM},
  title = {tagging, communities, vocabulary, evolution},
  url = {http://portal.acm.org/citation.cfm?id=1180904},
  year = 2006
}

@misc{santosneto07,
  author = {Santos-Neto, Elizeu and Ripeanu, Matei and Iamnitchi, Adriana},
  interhash = {a684fa9c2187ce048c69f6edb94f6c26},
  intrahash = {785df87d0942d1cb6da9b944df902730},
  title = {Tracking User Attention in Collaborative Tagging Communities},
  url = {http://arxiv.org/pdf/0705.1013},
  year = 2007
}

@inproceedings{magnani_visualtags_2006,
  abstract = {We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a web browser allows the user to observe and interact with the interesting tags as they evolve over time.New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.},
  address = {New York, NY, USA},
  author = {Dubinko, Micah and Kumar, Ravi and Magnani, Joseph and Novak, Jasmine and Raghavan, Prabhakar and Tomkins, Andrew},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  doi = {http://doi.acm.org/10.1145/1135777.1135810},
  interhash = {b9ff2f72831a1406013a86c8202d6276},
  intrahash = {cca8a679a78e2bced9a5cc268cfd3aaa},
  isbn = {1-59593-323-9},
  location = {Edinburgh, Scotland},
  pages = {193--202},
  publisher = {ACM Press},
  title = {Visualizing tags over time},
  url = {http://portal.acm.org/citation.cfm?id=1135810},
  year = 2006
}

@inproceedings{conf/ijcai/HayesAV07,
  author = {Hayes, Conor and Avesani, Paolo and Veeramachaneni, Sriharsha},
  booktitle = {IJCAI},
  crossref = {conf/ijcai/2007},
  date = {2007-03-05},
  editor = {Veloso, Manuela M.},
  ee = {http://www.ijcai.org/papers07/Papers/IJCAI07-445.pdf},
  interhash = {d569f2e7b70e9299a32533305055ca65},
  intrahash = {07ee256c7cd4433fc0bde4002c7bc90c},
  pages = {2772-2777},
  title = {An Analysis of the Use of Tags in a Blog Recommender System.},
  url = {www.ijcai.org/papers07/Papers/IJCAI07-445.pdf },
  year = 2007
}

@inproceedings{jaeschke2007tag,
  abstract = {Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.},
  address = {Berlin, Heidelberg},
  author = {Jäschke, Robert and Marinho, Leandro Balby and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  booktitle = {Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases},
  editor = {Kok, Joost N. and Koronacki, Jacek and de Mántaras, Ramon López and Matwin, Stan and Mladenic, Dunja and Skowron, Andrzej},
  ee = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  interhash = {7e212e3bac146d406035adebff248371},
  intrahash = {bb8ecec699a2f129322fe334747c6aef},
  isbn = {978-3-540-74975-2},
  pages = {506-514},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Tag Recommendations in Folksonomies},
  url = {http://dx.doi.org/10.1007/978-3-540-74976-9_52},
  volume = 4702,
  year = 2007
}

@misc{golder05structure,
  author = {Golder, Scott and Huberman, Bernardo A.},
  citeulike-article-id = {305755},
  eprint = {cs.DL/0508082},
  interhash = {2d312240f16eba52c5d73332bc868b95},
  intrahash = {f852d7a909fa3edceb04abb7d2a20f71},
  month = Aug,
  priority = {2},
  title = {The Structure of Collaborative Tagging Systems},
  url = {http://arxiv.org/abs/cs.DL/0508082},
  year = 2005
}

@inproceedings{jaeschke2006wege,
  abstract = {Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "`Web 2.0"'  -- stellen
Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und
mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen
ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur 
Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.},
  address = {Halle-Wittenberg},
  author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. 18. Workshop Grundlagen von Datenbanken},
  editor = {Braß, Stefan and Hinneburg, Alexander},
  interhash = {59224b5889a24108434a9b5ecc6b0887},
  intrahash = {2b6be3bd5daee7119973fcf69909956f},
  month = {June},
  pages = {80-84},
  publisher = {Martin-Luther-Universität },
  title = {Wege zur Entdeckung von Communities in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006wege.pdf},
  year = 2006
}

@inproceedings{Mika2005,
  author = {Mika, Peter},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  doi = {10.1007/11574620_38},
  ee = {http://dx.doi.org/10.1007/11574620_38},
  interhash = {5ea12110b5bb0e3a8ad09aeb16a70cdb},
  intrahash = {6929599cc8fbdc408282907eeec37204},
  owner = {mlux},
  pages = {522-536},
  publisher = {Springer},
  series = {LNCS},
  timestamp = {2006.01.19},
  title = {Ontologies Are Us: A Unified Model of Social Networks and Semantics},
  url = {http://dx.doi.org/10.1007/11574620_38},
  year = 2005
}