@inbook{hotho2008bookmarking,
        title = {Social Bookmarking},
        address = {München},
        author = {Andreas Hotho},
        booktitle = {Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software},
        editor = {Andrea Back and Norbert Gronau and Klaus Tochtermann},
        pages = {26-38},
        publisher = {Oldenbourg Verlag},
        year = 2008,
        url = {http://www.amazon.de/gp/redirect.html%3FASIN=3486585797%26tag=ws%26lcode=xm2%26cID=2025%26ccmID=165953%26location=/Web-2-0-Unternehmenspraxis-Grundlagen-Fallstudien/dp/3486585797%253FSubscriptionId=13CT5CVB80YFWJEPWS02},
        ean = {9783486585797},
	asin = {3486585797},
	isbn = {9783486585797},
	biburl = {http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/hotho},
	keywords = {2008 myown social folksonomy bookmarking}
}

@article{jaeschke2008discovering,
        title = {Discovering Shared Conceptualizations in Folksonomies},
        author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme},
        booktitle = {Semantic Web and Web 2.0},
        journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
        month = {feb},
        note = { },
        number = 1,
        pages = {38--53},
        volume = 6,
        year = 2008,
        url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
        description = {ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomies},
	abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.},
	biburl = {http://www.bibsonomy.org/bibtex/263901930c137df0c2dad84075c564b14/hotho},
	keywords = {bibsonomy myown discovering 2008 concept formal analysis tagging folksonomy fca}
}

@article{voelker2008aeon,
        title = {AEON - An Approach to the Automatic Evaluation of Ontologies},
        author = {Johanna Völker and Denny Vrandecic and York Sure and Andreas Hotho},
        journal = {Journal of Applied Ontology},
        note = {to appear},
        year = 2008,
        url = {http://ontoware.org/projects/aeon/},
        description = {Institut AIFB - Publikation: AEON - An Approach to the Automatic Evaluation of Ontologies},
	biburl = {http://www.bibsonomy.org/bibtex/2ea55fe7088ef25cdf060d30d94a09e26/hotho},
	keywords = {ml automatic sw evaluation ontology myown 2008}
}

@inproceedings{krause2008comparison,
        title = {A Comparison of Social Bookmarking  with Traditional Search},
        author = {Beate Krause and Andreas Hotho and Gerd Stumme},
        booktitle = {Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008},
        pages = {101-113},
        publisher = {Springer},
        volume = 4956,
        year = 2008,
        url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/ecir2008krause.pdf},
        abstract = {Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. 

In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.

Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.},
	biburl = {http://www.bibsonomy.org/bibtex/2613f5c41ff759fc548c9085102d1c933/hotho},
	keywords = {myown log search query 2008 social bookmarking}
}

@misc{cattuto-2008,
        title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging   Systems},
        author = {Ciro Cattuto and Dominik Benz and Andreas Hotho and Gerd Stumme},
        year = 2008,
        url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045},
        description = {[0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems},
	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.},
	biburl = {http://www.bibsonomy.org/bibtex/278fd64c3db55e6387ebdeb6c40054542/hotho},
	keywords = {ontology tag semantic learning myown analysis 2008 similarity ol}
}

@inproceedings{anti2008krause,
        title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems},
        author = {Beate Krause and Andreas Hotho and Gerd Stumme},
        booktitle = {Proc. of the Fourth International Workshop on  Adversarial Information Retrieval on the Web},
        year = 2008,
        url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/hotho},
	keywords = {dm myown classification mining folksonomy 2008 spam bookmarking ml social}
}

@inproceedings{Jaeschke2008logsonomy,
        title = {Logsonomy — A Search Engine Folksonomy},
        author = {Robert Jäschke and Beate Krause and Andreas Hotho and Gerd Stumme},
        booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
        publisher = {AAAI Press},
        year = 2008,
        url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf},
        abstract = {In social bookmarking systems users describe bookmarks
by keywords called tags. The structure behind
these social systems, called folksonomies, can be
viewed as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows specific
structural properties that explain its growth and the possibility
of serendipitous exploration.
Search engines filter the vast information of the web.
Queries describe a user’s information need. In response
to the displayed results of the search engine, users click
on the links of the result page as they expect the answer
to be of relevance. The clickdata can be represented as a
folksonomy in which queries are descriptions of clicked
URLs. This poster analyzes the topological characteristics
of the resulting tripartite hypergraph of queries,
users and bookmarks of two query logs and compares it
two a snapshot of the folksonomy del.icio.us.},
	biburl = {http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/hotho},
	keywords = {query icwsm logsonomy folksonomy 2008 myown analysis search log network}
}