@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
}

@proceedings{berendt2004web,
  address = {Heidelberg},
  editor = {Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and van Someren, Maarten and Spiliopoulou, Myra and Stumme, Gerd},
  interhash = {d7c78aa71fa7487dacfd9172467f1912},
  intrahash = {be8b91e0c05dde74a4af7123b949c6ac},
  isbn = {3-540-23258-3},
  note = {http://km.aifb.uni-karlsruhe.de/ws/ewmf03/},
  publisher = {Springer},
  series = {LNAI},
  title = {Web Mining: From Web to Semantic Web, First European Web
               Mining Forum, EMWF 2003, Cavtat-Dubrovnik, Croatia, September
               22, 2003, Revised Selected and Invited Papers},
  url = {http://springerlink.metapress.com/content/unvvag26dttf/},
  volume = 3209,
  year = 2004
}

@inproceedings{berendt2004roadmap,
  abstract = {The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science.},
  address = {Heidelberg},
  author = {Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and van Someren, Maarten and Spiliopoulou, Myra and Stumme, Gerd},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {Web Mining: From Web to Semantic Web},
  crossref = {DBLP:conf/ewmf/2003},
  editor = {Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and van Someren, Maarten and Spiliopoulou, Myra and Stumme, Gerd},
  ee = {http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=3209{\&}spage=1},
  interhash = {a27cc056d337718c1e10a3f9e3048143},
  intrahash = {158a99f0088fcefa45b1eb7f96aa5673},
  pages = {1-22},
  publisher = {Springer},
  title = {A Roadmap for Web Mining: From Web to Semantic Web.},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2004/berendt2004roadmap.pdf},
  volume = 3209,
  year = 2004
}

@incollection{berendt04usage,
  abstract = {Semantic Web Mining aims at combining the two fast-developing
research areas Semantic Web and Web Mining.
Web Mining aims at discovering insights about the meaning of Web
resources and their usage.  Given the primarily syntactical nature
of data Web mining operates on, the discovery of meaning is
impossible based on these data only. Therefore, formalizations of
the semantics of Web resources and navigation behavior are
increasingly being used.  This fits exactly with the aims of the
Semantic Web: the Semantic Web enriches the WWW by
machine-processable information which supports the user in his
tasks. In this paper, we discuss the interplay of the Semantic Web
with Web Mining, with a specific focus on usage mining.},
  address = {Boston},
  author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Data Mining  Next Generation Challenges and Future Directions},
  editor = {Kargupta, Hillol and Joshi, Anupam and Sivakumar, Krishnamoorthy and Yesha, Yelena},
  interhash = {272317659fc9bad03d6083a7afc1bc35},
  intrahash = {0ef00fe39718eae61dca4d251b14578d},
  isbn = {0-262-61203-8},
  pages = {461-481},
  publisher = {AAAI Press},
  title = {Usage Mining for and on the Semantic Web},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2004/berendt04usage.pdf},
  year = 2004
}

@inproceedings{agarwal03semantic,
  abstract = {The paper describes a set of approaches for representing and
accessing information within a semantically structured information
portal, while offering the possibility to integrate own
information. It discusses research performed within the project
`Semantic Methods and Tools for Information Portals (SemIPort)'.
In particular, it focuses on  (1) the development of scalable
storing, processing and querying methods for semantic data, (2)
visualization and browsing of complex data inventories, (3)
personalization and agent-based interaction, and (4) the
enhancement of web mining approaches for use within a
semantics-based portal.},
  address = {Bonn},
  author = {Agarwal, Sudhir and Fankhauser, Peter and Gonzalez-Ollala, Jorge and Hartmann, Jens and Hollfelder, Silvia and Jameson, Anthony and Klink, Stefan and Lehti, Patrick and Ley, Michael and Rabbidge, Emma and Schwarzkopf, Eric and Shrestha, Nitesh and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Walter, Bernd},
  booktitle = {INFORMATIK 2003 -- Innovative Informatikanwendungen (Band 1)},
  comment = {alpha},
  editor = {Dittrich, K. and König, W. and Oberweis, A. and Rannenberg, K. and Wahlster, W.},
  interhash = {54f275f02db30eafdab2f178e50fd7dc},
  intrahash = {8f2983e0f20c26ff98577059343f2cd4},
  pages = {116-131},
  publisher = {Gesellschaft für Informatik},
  series = {LNI},
  title = {Semantic Methods and Tools for Information Portals},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/agarwal2003semantic.pdf},
  volume = 34,
  year = 2003
}

@techreport{hotho03textclustering,
  abstract = {Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.},
  author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
  comment = {alpha},
  institution = {University of Karlsruhe, Institute AIFB},
  interhash = {0bc7c3fc1273355f45c8970a7ea58f97},
  intrahash = {61d58db419af0dbc3681432588219c3d},
  title = {Text Clustering Based on Background Knowledge},
  type = {Technical Report },
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf},
  volume = 425,
  year = 2003
}

@inproceedings{stumme02usage,
  address = {Baltimore},
  author = {Stumme, G. and Berendt, B. and Hotho, A.},
  booktitle = {Proc. NSF Workshop on Next Generation Data Mining},
  comment = {alpha},
  interhash = {479de77764be1ec66534be1c647e0857},
  intrahash = {4a68d1443065dcd7980989e97cb0af69},
  month = {November},
  pages = {77-86},
  title = {Usage Mining for and on the Semantic Web},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/NSF-NGDM02.pdf},
  year = 2002
}

@proceedings{berendt02semantic,
  address = {Helsinki, Finland},
  comment = {alpha},
  editor = {Berendt, B. and Hotho, A. and Stumme, G.},
  interhash = {d57b987c3937ff9d7ee64152cec01efa},
  intrahash = {b4d1e0ba321cc53504b66fc58ff18446},
  month = {August 19,},
  title = {Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf. on
                 Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and
                 Practice of Knowledge Discovery in Databases (PKDD'02)},
  url = {http://km.aifb.uni-karlsruhe.de/ws/semwebmine2002/online_html},
  year = 2002
}

@inproceedings{berendt02towards,
  address = {Heidelberg},
  author = {Berendt, B. and Hotho, A. and Stumme, G.},
  booktitle = {The Semantic Web -- ISWC 2002},
  comment = {alpha},
  editor = {Horrocks, I. and Hendler, J.},
  interhash = {4dd40c50089d3b86fb235bfaf3c8bee7},
  intrahash = {fc1c88be5f8c2640ca6e9a40b5fa1c7b},
  pages = {264-278},
  publisher = {Springer},
  series = {LNCS},
  title = {Towards Semantic Web Mining},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/ISWC02.pdf},
  year = 2002
}

@incollection{stumme02using,
  address = {Heidelberg},
  author = {Stumme, G.},
  booktitle = {Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung},
  comment = {alpha},
  editor = {Becker, J. and Knackstedt, R.},
  interhash = {89b56b4b45d3c9256355080ce94045e0},
  intrahash = {8cd4f719765abd1c46d28f200327d935},
  pages = {163-174},
  publisher = {Physica},
  title = {Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/REFMOD01.ps},
  year = 2002
}

@inproceedings{gonzalez02semantic,
  address = {Helsinki},
  author = {Gonzalez-Olalla, J. and Stumme, G.},
  booktitle = {Semantic Web Mining. Proc. of the Semantic Web Mining Workshop of the 13th Europ. Conf.},
  comment = {alpha},
  editor = {Berendt, B. and Hotho, A. and Stumme, G.},
  interhash = {f66eaa20bfcd4b4b57a8ff851a4a74aa},
  intrahash = {c9fdc0e65bbd2a49ee0b1396b068a76d},
  month = {August 19,},
  pages = 90,
  title = {Semantic Methods and Tools for Information Portals - The {S}em{IP}ort Project (Project Description)},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/gonzalez2002semantic.pdf},
  year = 2002
}

@inproceedings{hartmann02semanticweb,
  address = {Oldenburg},
  author = {Hartmann, J. and Hotho, A. and Stumme, G.},
  booktitle = {Proc. Arbeitskreistreffen Knowledge Discovery},
  comment = {alpha},
  interhash = {c07545febc9e7b32803bf33547ec9004},
  intrahash = {a5f1a8b42409b96271bc5c671deceea9},
  month = {September},
  title = {Semantic Web Mining for Building Information Portals (Position Paper)},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/hartmann2002semanticweb.pdf},
  year = 2002
}

@proceedings{alani2006proceedings,
  editor = {Alani, Harith and Hoser, Bettina and Schmitz, Christoph and Stumme, Gerd},
  interhash = {e991143409a8f4acb9eabfe08a38e387},
  intrahash = {786a452a14c5189d82dc56f16cc2a266},
  title = {Proceedings of the 2nd Workshop on Semantic Network Analysis},
  url = {http://www.kde.cs.uni-kassel.de/ws/sna2006/},
  year = 2006
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.},
  interhash = {20650d852ca3b82523fcd8b63e7c12d7},
  intrahash = {11b2a59a568d246d7f36cb68169a464a},
  month = {July},
  pages = {261--270},
  publisher = {Springer},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  title = {Mining Association Rules in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf},
  year = 2006
}

@proceedings{stumme05semanticnetworkanalysis,
  address = {Aachen},
  editor = {Stumme, Gerd and Hoser, Bettina and Schmitz, Christoph and Alani, Harith},
  interhash = {6316cb226778a6a6f156821f975b2ba3},
  intrahash = {c44763991d44182c53606a2c93054f26},
  issn = {1613-0073},
  publisher = {CEUR Proceedings},
  title = {Proceedings of the First Workshop on Semantic Network Analysis },
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/},
  year = 2005
}

@inproceedings{berendt05semantic,
  author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space},
  editor = {Svatek, Vojtech and Snasel, Vaclav},
  interhash = {e4b853ff13b63a88b009610890b89348},
  intrahash = {f8826ba2790eeb857dd4becb31a08225},
  isbn = {80-248-0864-1},
  pages = {1-16},
  publisher = {Technical University of Ostrava},
  title = {Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/berendt2005semantic.pdf},
  year = 2005
}

@inproceedings{stumme01bottom,
  address = {Seattle, WA, USA},
  author = {Stumme, G. and Maedche, A.},
  booktitle = {Proc. 17th Intl. Conf. on Artificial Intelligence (IJCAI '01)},
  comment = {alpha},
  editor = {Nebel, B.},
  interhash = {2ff2aa6492ba1857d18042fa4d57111d},
  intrahash = {cd6d6c64f79d13d7cf394d3a6b3858ba},
  pages = {225-230},
  title = {{FCA}-{M}erge: Bottom-Up Merging of Ontologies.},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/IJCAI01.pdf},
  year = 2001
}

@inproceedings{stumme00towardsanorder,
  address = {Aachen},
  author = {Stumme, G. and Studer, R. and Sure, Y.},
  booktitle = {Verbundtagung Wirtschaftsinformatik 2000},
  comment = {alpha},
  editor = {Bodendorf, F. and Grauer, M.},
  interhash = {9042498686a1b2c000cd4b0007a76bf8},
  intrahash = {83cc12ae3979a3e4108e5442b7da0b4f},
  pages = {136-149},
  publisher = {Shaker},
  title = {Towards an Order-Theoretical Foundation for Maintaining and Merging Ontologies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/REFMOD00.pdf},
  year = 2000
}

@inproceedings{studer03building,
  address = {Osaka, Japan},
  author = {Studer, Rudi and Stumme, Gerd and Handschuh, Siegfried and Hotho, Andreas and Motik, B.},
  booktitle = {New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational},
  comment = {alpha},
  interhash = {67d164f4a531ddf0f84df0b5de52e80a},
  intrahash = {a0e7b52680f1876cdd9cd21f7cb2f95c},
  month = {March 10-11,},
  pages = {31-34},
  title = {Building and Using the Semantic Web},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/Sanken03.pdf},
  year = 2003
}

@inproceedings{hotho03explaining,
  abstract = {Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
 In this paper, we discuss a way of integrating a large thesaurus
 and the computation of lattices of resulting clusters  into common text clustering
 in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.},
  address = {Heidelberg},
  author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
  booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases},
  comment = {alpha},
  editor = {Lavra\v{c}, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco},
  interhash = {cf66183151a5d94a0941ac6d5089ae89},
  intrahash = {53a943b6be4b34cf4e5329d0b58e99f6},
  pages = {217-228},
  publisher = {Springer},
  series = {LNAI},
  title = {Explaining Text Clustering Results using  Semantic  Structures},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf},
  volume = 2838,
  year = 2003
}

@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
}

@book{Semantic2006Ackermann,
  address = {Heidelberg},
  editor = {Ackermann, Markus and Berendt, Bettina and Grobelnik, Marko and Hotho, Andreas and Mladenic, Dunja and Semeraro, Giovanni and Spiliopoulou, Myra and Stumme, Gerd and Svatek, Vojtech and van Someren, Maarten},
  interhash = {f4891d2967571b77f1276298f112158f},
  intrahash = {f8335dd42e7eec61d60bbeced71ff3be},
  isbn13 = {978-3-540-47697-9},
  issn = {0302-9743},
  publisher = {Springer},
  title = {Semantics, Web and Mining},
  url = {http://dx.doi.org/10.1007/11908678},
  year = 2006
}

@proceedings{stumme01semantic,
  address = {Freiburg},
  editor = {Stumme, G. and Hotho, A. and Berendt, B.},
  interhash = {8e0ea487e66cca158e69263d18d856f8},
  intrahash = {ee4f8a6316e6454df268067b4e47f818},
  month = {September 3rd,},
  title = {Semantic Web Mining. Workshop Proceedings.},
  url = {http://semwebmine2001.aifb.uni-karlsruhe.de/online.html},
  year = 2001
}

@inproceedings{schmitz02accessing,
  address = {Norfolk},
  author = {Schmitz, C. and Staab, S. and Studer, R. and Stumme, G. and Tane, J.},
  booktitle = {Proc. of E-Learning 2002 World Conference on E-Learning in Corporate,     Government, Healthcare and Higher Education on (E-Learning 2002)},
  editor = {Driscoll, M. and Reeves, T.C.},
  interhash = {654b9a08b2bc09cf44dfe51840371e23},
  intrahash = {d366e42880a2d2991cbfa6abc53b8fe4},
  note = {{A}warded paper},
  pages = {909-915},
  title = {Accessing Distributed Learning Repositories through a Courseware
    Watchdog},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/E-Learn02.pdf},
  volume = {AACE},
  year = 2002
}

@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{benz2009characterizing,
  address = {Bled, Slovenia},
  author = {Benz, Dominik and Krause, Beate and Kumar, G. Praveen and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)},
  interhash = {de5e58b26200e44112d9791f39e7523d},
  intrahash = {b697a98a7340585594455ee2e81d238a},
  month = {September},
  title = {Characterizing Semantic Relatedness of Search Query Terms},
  year = 2009
}

@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
}

@book{berendt09knowledge,
  address = {Heidelberg},
  author = {?},
  interhash = {12fa8ec01220bd1e437a945ea1972a09},
  intrahash = {d3aee79927e4d00aa1408d2a26749778},
  note = {(to appear)},
  publisher = {Springer},
  title = {Knowledge Discovery Enhanced with Semantic and Social Information},
  year = {2009 }
}

@misc{hirst97lexical,
  author = {Hirst, G. and St-Onge, D.},
  interhash = {be945221d6d8888cffe5d9ede713d15f},
  intrahash = {c7017d380511206230432d580b457426},
  text = {Hirst G., St-Onge D. 1997. Lexical Chains as representation of context
    for the detection and correction malapropisms. In C. Fellbaum, editor, WordNet:
    An electronic lexical database and some of its applications. Cambrige, MA:
    The MIT Press.},
  title = {Lexical Chains as representation of context for the detection and correction
    malapropisms},
  url = {citeseer.ist.psu.edu/hirst97lexical.html},
  year = 1997
}

@article{mohammadSubmittedDistributional,
  author = {Mohammad, Saif and Hirst, Graeme},
  interhash = {69e8fa5785216419a3f39536115b814e},
  intrahash = {fe1ed4dfc0e42165de44853564c7f6af},
  title = {Distributional measures as proxies for semantic relatedness},
  url = {http://ftp.cs.toronto.edu/pub/gh/Mohammad+Hirst-2005.pdf},
  year = {Submitted for publication}
}

@book{harris68mathematical,
  address = {New York},
  author = {Harris, Z. S.},
  interhash = {a2f232a3806a54bf3770d35ce62347a5},
  intrahash = {f834ac9131f49062a5f1e362c35c8de8},
  location = {New York},
  publisher = {Wiley},
  title = {Mathematical Structures of Language},
  year = 1968
}

@inproceedings{Resnik95,
  author = {Resnik, Philip},
  booktitle = {Proceedings of the XI International Joint Conferences on Artificial},
  interhash = {746146003bcba4f1df57044178a1b9ac},
  intrahash = {8edbd78ec00b2649fd6b6b5a85fc03ff},
  pages = {448-453},
  title = {{Using Information Content to Evaluate Semantic Similarity in a Taxonomy}},
  year = 1995
}

@article{budanitsky06evaluating,
  author = {Budanitsky, Alexander and Hirst, Graeme},
  date = {2006-07-25},
  ee = {http://dx.doi.org/10.1162/coli.2006.32.1.13},
  interhash = {a259f21d89bdc61a64ce11a3aea0af06},
  intrahash = {cfd7c510a33c5cb7a3d69aa5c18cc8ed},
  journal = {Computational Linguistics},
  number = 1,
  pages = {13-47},
  title = {Evaluating WordNet-based Measures of Lexical Semantic Relatedness},
  url = {http://dblp.uni-trier.de/db/journals/coling/coling32.html#BudanitskyH06},
  volume = 32,
  year = 2006
}

@inbook{schmitz2006kollaboratives,
  abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert
einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft},
  editor = {Pellegrini, Tassilo and Blumauer, Andreas},
  interhash = {cc0f3d4fa8f36968f02837e3f9f5c57b},
  intrahash = {53e13744981f2c04d9239e0cf9b4e689},
  isbn = {3-540-29324-8},
  pages = {273-290},
  publisher = {Springer},
  title = {Kollaboratives Wissensmanagement},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf},
  year = 2006
}

@book{pellegrini2006semantic,
  address = {Heidelberg},
  editor = {Pellegrini, T. and Blumauer, A.},
  interhash = {524d047f63a4d1ecb5e0776636758196},
  intrahash = {32ba5b64f36827a247ff3e44eca09a08},
  publisher = {Springer},
  title = {Semantic Web: Wege zur vernetzten Wissensgesellschaft},
  year = 2006
}

@proceedings{DBLP:conf/esws/2006,
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {ESWC},
  editor = {Sure, York and Domingue, John},
  interhash = {282f1283e2e59a84f03e7fc9235fc933},
  intrahash = {7feddd95c6e6d3db309421a83088a3da},
  isbn = {3-540-34544-2},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {The Semantic Web: Research and Applications, 3rd European
               Semantic Web Conference, ESWC 2006, Budva, Montenegro, June
               11-14, 2006, Proceedings},
  volume = 4011,
  year = 2006
}

@inproceedings{DBLP:conf/esws/AssemMMS06,
  author = {van Assem, Mark and Malais{\'e}, V{\'e}ronique and Miles, Alistair and Schreiber, Guus},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {ESWC},
  crossref = {DBLP:conf/esws/2006},
  editor = {Sure, York and Domingue, John},
  ee = {http://dx.doi.org/10.1007/11762256_10},
  interhash = {ce44953b51e17605040d608a353ac84d},
  intrahash = {f9d4a1f59e794880d06266746822075d},
  isbn = {3-540-34544-2},
  pages = {95-109},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Method to Convert Thesauri to SKOS.},
  volume = 4011,
  year = 2006
}

@inproceedings{DBLP:conf/esws/SerafiniZSB06,
  author = {Serafini, Luciano and Zanobini, Stefano and Sceffer, Simone and Bouquet, Paolo},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {ESWC},
  crossref = {DBLP:conf/esws/2006},
  ee = {http://dx.doi.org/10.1007/11762256_4},
  interhash = {084af48010f71f02ed3e3c4db6a054e2},
  intrahash = {5306cd7f1d989d6eb0873ec425c6f223},
  pages = {4-18},
  title = {Matching Hierarchical Classifications with Attributes.},
  year = 2006
}

@inproceedings{DBLP:conf/esws/NovacekS06,
  author = {Nov{\'a}cek, V\'{\i}t and Smrz, Pavel},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {ESWC},
  crossref = {DBLP:conf/esws/2006},
  ee = {http://dx.doi.org/10.1007/11762256_8},
  interhash = {7c22b562cb44a73a89b1f92e94edab1a},
  intrahash = {2eee171e9a0fe2dd8002634ae5890ddf},
  pages = {65-79},
  title = {Empirical Merging of Ontologies - A Proposal of Universal Uncertainty Representation Framework.},
  year = 2006
}