@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
}

@inproceedings{Strube:2006:WCS:1597348.1597414,
  abstract = {Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.},
  acmid = {1597414},
  author = {Strube, Michael and Ponzetto, Simone Paolo},
  booktitle = {proceedings of the 21st national conference on Artificial intelligence - Volume 2},
  interhash = {a09d5123ab9ab8cb00b8df6f0a7f5c81},
  intrahash = {3ed30e1d22b977de9a4a50c929200d2d},
  isbn = {978-1-57735-281-5},
  location = {Boston, Massachusetts},
  numpages = {6},
  pages = {1419--1424},
  publisher = {AAAI Press},
  series = {AAAI'06},
  title = {WikiRelate! computing semantic relatedness using wikipedia},
  url = {http://dl.acm.org/citation.cfm?id=1597348.1597414},
  year = 2006
}

@inproceedings{bozsak2002towards,
  author = {Bozsak, E. and Ehrig, Marc and Handschuh, Siegfried and Hotho, Andreas and Maedche, Alexander and Motik, Boris and Oberle, Daniel and Schmitz, Christoph and Staab, Steffen and Stojanovic, Ljiljana and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Sure, York and Tane, Julien and Volz, Raphael and Zacharias, Valentin},
  booktitle = {Proceedings of the Third International Conference on E-Commerce and Web Technologies (EC-Web 2002), Aix-en-Provence, France},
  editor = {Bauknecht, Kurt and Tjoa, A. Min and Quirchmayr, Gerald},
  interhash = {940750309ac472ea48a712e16b5d902a},
  intrahash = {d0aa1d2d01e378046e1693babc026836},
  pages = {304-313},
  publisher = {Springer},
  series = {LNCS},
  title = {KAON - Towards a large scale Semantic Web},
  url = {http://www.aifb.uni-karlsruhe.de/WBS/ysu/publications/2002_ecweb_kaon.pdf},
  volume = 2455,
  year = 2002
}

@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
}

@inproceedings{eisterlehner2010visit,
  address = {Toronto, Canada},
  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},
  interhash = {5584c4c57fcd8eb4663df8b114bcf09c},
  intrahash = {a97c4f7e80dcb666450acf697002155e},
  title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy},
  year = 2010
}

@article{berendt2010bridging,
  author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd},
  doi = {DOI: 10.1016/j.websem.2010.04.008},
  interhash = {4969eb2b7bf1fabe60c5f23ab6383d77},
  intrahash = {f8d7bc2af5753906dc3897196daac18c},
  issn = {1570-8268},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences},
  number = {2-3},
  pages = {95 - 96},
  title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0},
  url = {http://www.sciencedirect.com/science/article/B758F-4YXK4HW-1/2/4cb514565477c54160b5e6eb716c32d7},
  volume = 8,
  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
}

@article{jws2006Semantic,
  abstract = {SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
improving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
Web itself.
The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
these resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
formalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
integration could be profitable.},
  author = {Stumme, Gerd and Hotho, Andreas and Berendt, Bettina},
  interhash = {3fd4efcf649ab35e8ef001f19b7ff83c},
  intrahash = {9937253e6b2591267a0596fa597a4b96},
  journal = {Journal of Web Semantics},
  number = 2,
  pages = {124-143},
  publisher = {Elsevier},
  title = {Semantic Web Mining - State of the Art and Future Directions},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/stumme2006semantic.pdf},
  volume = 4,
  year = 2006
}

@inproceedings{hoser2006semantic,
  abstract = {A key argument for modeling knowledge in ontologies is the easy
re-use and re-engineering of the knowledge. However, beside
consistency checking, current ontology engineering tools provide
only basic functionalities for analyzing ontologies. Since
ontologies can be considered as (labeled, directed) graphs, graph
analysis techniques are a suitable answer for this need. Graph
analysis has been performed by sociologists for over 60 years, and
resulted in the vivid research area of Social Network Analysis
(SNA). While social network structures in general currently receive
high attention in the Semantic Web community, there are only very
few SNA applications up to now, and virtually none for analyzing the
structure of ontologies.

We illustrate in this paper the benefits of applying SNA to
ontologies and the Semantic Web, and discuss which research topics
arise on the edge between the two areas. In particular, we discuss
how different notions of centrality describe the core content and
structure of an ontology. From the rather simple notion of degree
centrality over betweenness centrality to the more complex
eigenvector centrality based on Hermitian matrices, we illustrate
the insights these measures provide on two ontologies, which are
different in purpose, scope, and size.},
  address = {Heidelberg},
  author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {344ec3b4ee8af1a2c6b86efc14917fa9},
  intrahash = {c0cdbeab23ce0fc1bff5e02c99aab012},
  month = {June},
  pages = {514-529},
  publisher = {Springer},
  series = {LNAI},
  title = {Semantic Network Analysis of Ontologies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf},
  volume = 4011,
  year = 2006
}

@inproceedings{tane04semantic,
  author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {Proc. 13th International World Wide Web Conference (WWW 2004)},
  ee = {http://doi.acm.org/10.1145/1013369},
  interhash = {92089bd1e3e798b50fe7c82cd8333e7b},
  intrahash = {10efb9940c6f2881cd6c84b6c52bebd8},
  pages = {1-10},
  title = {Semantic resource management for the web: an e-learning application},
  url = {http://www.www2004.org/proceedings/docs/2p1.pdf},
  year = 2004
}