@article{cattuto2009collective,
  abstract = {The enormous increase of popularity and use of the worldwide web has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with keywords known as “tags.” Understanding the rich emergent structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks (RWs), and complex networks theory, can effectively contribute to the mathematical modeling of social annotation systems. Here, we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of RWs. This modeling framework reproduces several aspects, thus far unexplained, of social annotation, among which are the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and that are typically hard to access.},
  author = {Cattuto, Ciro and Barrat, Alain and Baldassarri, Andrea and Schehr, Gregory and Loreto, Vittorio},
  doi = {10.1073/pnas.0901136106},
  eprint = {http://www.pnas.org/content/106/26/10511.full.pdf+html},
  interhash = {0d9b41d0509bf9ee8004010663452a22},
  intrahash = {0d1491f34bcd6f29c0a59e449cfdffa1},
  journal = {Proceedings of the National Academy of Sciences},
  number = 26,
  pages = {10511-10515},
  title = {Collective dynamics of social annotation},
  url = {http://www.pnas.org/content/106/26/10511.abstract},
  volume = 106,
  year = 2009
}

@inproceedings{bullinaria2008semantic,
  author = {Bullinaria, J.A.},
  file = {bullinaria2008semantic.pdf:bullinaria2008semantic.pdf:PDF},
  groups = {public},
  interhash = {cdb7b1ff0e89f61f84e2c15a0e46c221},
  intrahash = {efae206c0f89363a3273a8d57c87eff5},
  journal = {ESSLLI Workshop on Distributional Lexical Semantics},
  timestamp = {2011-01-28 09:53:43},
  title = {Semantic Categorization Using Simple Word Co-occurrence statistics},
  username = {dbenz},
  year = 2008
}

@inproceedings{cantador2008enriching,
  abstract = {Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals’ tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites.},
  author = {Cantador, Ivan and Szomszor, Martin and Alani, Harith and Fernandez, Miriam and Castells, Pablo},
  booktitle = {1st International Workshop on Collective Semantics: Collective Intelligence \& the Semantic Web (CISWeb 2008) },
  file = {cantador2008enriching.pdf:cantador2008enriching.pdf:PDF},
  groups = {public},
  interhash = {b201967f2e9ef8e8907f18fe139a306b},
  intrahash = {00806894ae96282af699a8d87453d9fd},
  month = {June},
  timestamp = {2011-02-17 10:58:34},
  title = {Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations},
  url = {http://eprints.ecs.soton.ac.uk/15451/},
  username = {dbenz},
  year = 2008
}

@inproceedings{gabrilovich2007computing,
  abstract = {Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0:56 to 0:75 for individual words and from r = 0:60 to 0:72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.},
  author = {Gabrilovich, E. and Markovitch, S.},
  booktitle = {Proceedings of the 20th International Joint Conference on Artificial Intelligence},
  file = {gabrilovich2007computing.pdf:gabrilovich2007computing.pdf:PDF},
  groups = {public},
  interhash = {5baf6af4bf58cf3926b39a12edb35e58},
  intrahash = {839a06f838f02c04a8569fd41a5da284},
  pages = {6--12},
  timestamp = {2010-08-16 14:11:53},
  title = {Computing semantic relatedness using wikipedia-based explicit semantic analysis},
  url = {http://scholar.google.de/scholar.bib?q=info:woCrRNTAsA4J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=3},
  username = {dbenz},
  year = 2007
}

@inproceedings{specia2007integrating,
  abstract = {While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.},
  author = {Specia, Lucia and Motta, Enrico},
  file = {specia2007integrating.pdf:specia2007integrating.pdf:PDF},
  groups = {public},
  interhash = {b828fbd5c9ddc4f9551f973445ecb283},
  intrahash = {8800fc1a639aeb43fd55598d2410e2e1},
  pages = {624-639},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  timestamp = {2007-09-29 15:16:09},
  title = {Integrating Folksonomies with the Semantic Web},
  username = {dbenz},
  volume = {4519/2007},
  year = 2007
}

@inproceedings{tesconi2008semantify,
  abstract = {At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. 1},
  author = {Tesconi, Maurizio and Ronzano, Francesco and Marchetti, Andrea and Minutoli, Salvatore},
  booktitle = {Proceedings of the Workshop Social Data on the Web (SDoW2008)},
  crossref = {CEUR-WS.org/Vol-405},
  file = {tesconi2008semantify.pdf:tesconi2008semantify.pdf:PDF},
  groups = {public},
  interhash = {0c1c96b41a0af8512c20a7d41504640f},
  intrahash = {dd698b5ee4d93496d11627cbe1615514},
  timestamp = {2009-09-27 15:57:13},
  title = {Semantify del.icio.us: Automatically Turn your Tags into Senses},
  url = {http://CEUR-WS.org/Vol-405/paper8.pdf},
  username = {dbenz},
  year = 2008
}

@article{ryu2009toward,
  abstract = {This paper describes new thesaurus construction method in which class-based, small size thesauruses are constructed and merged as a whole based on domain classification system. This method has advantages in that 1) taxonomy construction complexity is reduced, 2) each class-based thesaurus can be reused in other domain thesaurus, and 3) term distribution per classes in target domain is easily identified. The method is composed of three steps: term extraction step, term classification step, and taxonomy construction step. All steps are balanced approaches of automatic processing and manual verification. We constructed Korean IT domain thesaurus based on proposed method. Because terms are extracted from Korean newspaper and patent corpus in IT domain, the thesaurus includes many Korean neologisms. The thesaurus consists of 81 upper level classes and over 1,000 IT terms.},
  author = {Ryu, P.M. and Kim, J.H. and Nam, Y. and Huang, J.X. and Shin, S. and Lee, S.M. and Choi, K.S.},
  file = {ryu2009toward.pdf:ryu2009toward.pdf:PDF},
  groups = {public},
  interhash = {33037e9884a62f1994c9d45eb68c27e7},
  intrahash = {bd4f375366e49a3eb31e60b268dca01c},
  journal = {Relation},
  journalpub = {1},
  number = {1.129},
  pages = 7396,
  publisher = {Citeseer},
  timestamp = {2010-11-09 12:05:09},
  title = {{Toward Domain Specific Thesaurus Construction: Divide-and-Conquer Method}},
  url = {http://scholar.google.de/scholar.bib?q=info:4K_xIsqmea0J:scholar.google.com/&output=citation&hl=de&as_sdt=2000&ct=citation&cd=9},
  username = {dbenz},
  volume = 10,
  year = 2009
}

@inproceedings{heymann2010tagging,
  abstract = {A fundamental premise of tagging systems is that regular users can organize large collections for browsing and other tasks using uncontrolled vocabularies. Until now, that premise has remained relatively unexamined. Using library data, we test the tagging approach to organizing a collection. We find that tagging systems have three major large scale organizational features: consistency, quality, and completeness. In addition to testing these features, we present results suggesting that users produce tags similar to the topics designed by experts, that paid tagging can effectively supplement tags in a tagging system, and that information integration may be possible across tagging systems.},
  author = {Heymann, Paul and Paepcke, Andreas and Garcia-Molina, Hector},
  booktitle = {WSDM},
  crossref = {conf/wsdm/2010},
  date = {2010-02-18},
  editor = {Davison, Brian D. and Suel, Torsten and Craswell, Nick and Liu, Bing},
  ee = {http://doi.acm.org/10.1145/1718487.1718495},
  file = {:heyman2010tagging.pdf:PDF},
  groups = {public},
  interhash = {d4f72ed57e6b99dbe32e18e218d81ef5},
  intrahash = {12579231cd5449f9a40cba9924975f09},
  isbn = {978-1-60558-889-6},
  pages = {51-60},
  publisher = {ACM},
  timestamp = {2010-04-08 07:27:02},
  title = {Tagging human knowledge.},
  url = {http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#HeymannPG10},
  username = {dbenz},
  year = 2010
}

@inproceedings{abel2008benefit,
  abstract = {With the advent of Web 2.0 folksonomy systems like Flickr, del.icio.us, etc. have become very popular. They enable users to annotate resources (images, websites, etc.) with freely chosen keywords, so-called tags. The evolving set of such tag assignments, which are generally user-tag-resource bindings, are called folksonomies. Folksonomies embody valuable information that can be exploited by search and ranking algorithms. In this paper we describe our ongoing research in analyzing the benefit of additional semantics in folksonomy systems. We present the GroupMe! folksonomy system, which brings additional semantics to tagging systems by enabling users to group resources. We furthermore introduce different group-sensitive ranking algorithms that outperform existing folksonomy ranking strategies and define SocialHITS - a HITS-based algorithm to detect hubs and authorities in folksonomy systems.},
  address = {New York, NY, USA},
  author = {Abel, Fabian},
  booktitle = {PIKM '08: Proceeding of the 2nd PhD workshop on Information and knowledge management},
  doi = {http://doi.acm.org/10.1145/1458550.1458560},
  file = {abrams1997how.pdf:abrams1997how.pdf:PDF;abel2008benefit.pdf:abel2008benefit.pdf:PDF},
  groups = {tagora},
  interhash = {4a9d21e3405a26ab6c7e3c78f5eab98b},
  intrahash = {0276b8f54393fcfd8b24cd2a1f67ccaa},
  isbn = {978-1-60558-257-3},
  location = {Napa Valley, California, USA},
  pages = {49--56},
  publisher = {ACM},
  timestamp = {2010-01-18 20:11:55},
  title = {The benefit of additional semantics in folksonomy systems},
  url = {http://portal.acm.org/citation.cfm?id=1458560},
  username = {dbenz},
  year = 2008
}

@inproceedings{ankolekar2007two,
  abstract = {A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s strengths. We believe that future web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.},
  address = {New York, NY, USA},
  author = {Ankolekar, Anupriya and Krötzsch, Markus and Tran, Thanh and Vrandecic, Denny},
  booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web},
  doi = {http://doi.acm.org/10.1145/1242572.1242684},
  file = {ankolekar2007two.pdf:ankolekar2007two.pdf:PDF},
  groups = {public},
  interhash = {1e51bd6cd043142a8de98b93e82b68b1},
  intrahash = {6b493ae653fcff556997f30273d766b9},
  isbn = {978-1-59593-654-7},
  location = {Banff, Alberta, Canada},
  pages = {825--834},
  publisher = {ACM Press},
  timestamp = {2007-08-05 16:27:33},
  title = {The two cultures: mashing up web 2.0 and the semantic web},
  url = {http://portal.acm.org/citation.cfm?id=1242684&coll=GUIDE&dl=ACM&CFID=21633871&CFTOKEN=81037701},
  username = {dbenz},
  year = 2007
}

@inproceedings{braun2007ontology,
  abstract = {Ontology maturing as a conceptual process model is based on the assumption that ontology engineering is a continuous collaborative and informal learning process and always embedded in tasks that make use of the ontology to be developed. For supporting ontology maturing, we need lightweight and easy-to-use tools integrating usage and construction processes of ontologies. Within two applications – ImageNotion for semantic annotation of images and SOBOLEO for semantically enriched social bookmarking – we have shown that such ontology maturing support is feasible with the help of Web 2.0 technologies.In this paper, we want to present the conclusions from two evaluation sessions with end users and summarize requirements for further development.},
  author = {Braun, Simone and Schmidt, Andreas and Walter, Andreas and Zacharias, Valentin},
  booktitle = {IInternational Workshop on Emergent Semantics and Ontology Evolution (ESOE), 6th International Semantic Web Conference (ISWC 2007)},
  date = {November 10-15 2007},
  file = {braun2007ontology.pdf:braun2007ontology.pdf:PDF},
  groups = {public},
  interhash = {269df6cd5d992c8a88c11dda0ff34306},
  intrahash = {e618c6fd8ec3f0f7173a51d00c0a7e69},
  location = {Busan, Korea},
  timestamp = {2007-09-26 09:01:35},
  title = {The Ontology Maturing Approach to Collaborative and Work-Integrated Ontology Development: Evaluation Results and Future Directions},
  username = {dbenz},
  year = 2007
}

@inproceedings{braun2007soboleo,
  abstract = {Bisher gibt es kein integriertes Werkzeug, das sowohl die kollaborative Erstellung eines Indexes relevanter Internetressourcen („Social Bookmarking“) als auch einer gemeinsamen Ontologie, die zur Organisation des Indexes genutzt wird, integriert unterstützt. Derzeitige Werkzeuge gestatten entweder die Erstellung einer Ontologie oder die Strukturierung von Ressourcen entsprechend einer vorgegebenen, unveränderlichen Ontologie bzw. ganz ohne jegliche Struktur. In dieser Arbeit zeigen wir, wiesich kollaboratives Tagging und kollaborative Ontologieentwicklung vereinen lassen, so dass jeweiligeSchwächen vermieden werden und die Stärken einander ergänzen. Wir präsentieren SOBOLEO, ein System, das kollaborativ und web-basiert die Erstellung, Erweiterung und Pflege von Ontologien und gemeinsamer Lesezeichensammlung ermöglicht und gleichzeitig die Annotierung von Internetressourcen mit Konzepten aus der erstellten Ontologie unterstützt.},
  address = {München},
  author = {Braun, Simone and Schmidt, Andreas and Zacharias, Valentin},
  booktitle = {Mensch \& Computer - 7. Fachübergreifende Konferenz - M\&C 2007},
  editor = {Gross, Tom},
  file = {braun2007soboleo.pdf:braun2007soboleo.pdf:PDF},
  groups = {public},
  interhash = {31ee80a338477a8377537b4864959545},
  intrahash = {5712e57b4fbdd5e837e860f61744ac62},
  isbn = {978-3-486-58496-7},
  pages = {209-218},
  publisher = {Oldenbourg Verlag},
  timestamp = {2007-09-15 12:56:14},
  title = {SOBOLEO: vom kollaborativen Tagging zur leichtgewichtigen Ontologie},
  url = {http://publications.andreas.schmidt.name/Braun_Schmidt_Zacharias_SOBOLEO_MuC2007.pdf},
  username = {dbenz},
  year = 2007
}

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

@article{gemmell2008personalizing,
  abstract = {The popularity of collaborative tagging, otherwise known as “folksonomies�?, emanate from the flexibility they afford usersin navigating large information spaces for resources, tags, or other users, unencumbered by a pre-defined navigational orconceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: usersare free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous,or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregateuser models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervisedhierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clustersprovide the nexus between the user and those resources that correspond more closely to the user’s intent. We validate thisassertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site.},
  author = {Gemmell, Jonathan and Shepitsen, Andriy and Mobasher, Bamshad and Burke, Robin},
  file = {gemmell2008personalizing.pdf:gemmell2008personalizing.pdf:PDF},
  groups = {public},
  interhash = {e544ba095f411429896b11fd3f94fd5c},
  intrahash = {2e0535788c372e98e49646873cea4e1e},
  journal = {Data Warehousing and Knowledge Discovery},
  journalpub = {1},
  pages = {196--205},
  timestamp = {2009-08-10 10:30:08},
  title = {Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering},
  url = {http://dx.doi.org/10.1007/978-3-540-85836-2_19},
  username = {dbenz},
  year = 2008
}

@inproceedings{hjelm2008multilingual,
  abstract = {We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clustering based on distributional similarity of the terms in texts. We show that basing the clustering on a comparable corpus in four languages gives a considerable improvement in accuracy compared to using only the monolingual English texts. We also show that hierarchical k-means clustering increases the similarity to the original taxonomy, when compared with a bottom-up agglomerative clustering approach.},
  author = {Hjelm, Hans and Buitelaar, Paul},
  booktitle = {ECAI},
  crossref = {conf/ecai/2008},
  editor = {Ghallab, Malik and Spyropoulos, Constantine D. and Fakotakis, Nikos and Avouris, Nikolaos M.},
  ee = {http://dx.doi.org/10.3233/978-1-58603-891-5-288},
  file = {hjelm2008multilingual.pdf:hjelm2008multilingual.pdf:PDF},
  groups = {public},
  interhash = {21a658154fb1a02e773b7a678b15f9f4},
  intrahash = {813903a333a40ecf9a59ded552acb323},
  isbn = {978-1-58603-891-5},
  pages = {288-292},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  timestamp = {2011-01-18 12:06:01},
  title = {Multilingual Evidence Improves Clustering-based Taxonomy Extraction.},
  url = {http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf},
  username = {dbenz},
  volume = 178,
  year = 2008
}

@inproceedings{lalwani2009deriving,
  abstract = {In this paper we describe our investigation of tagging systems and the derivation of ontological structure in the form of a folksonomy from the set of tags. Tagging systems are becoming popular, because the amount of information available on some websites is becoming too large for humans to browse manually and the types of information (multimedia data) is unsuitable for the indexers used by conventional search engines to organize. However, tag-based search is very inaccurate and incomplete (low precision and recall), because the semantics of the tags is both weak and ambiguous. The basic problem is that tags are treated like keywords by search engines, which consider individual tags in isolation. However, there is additional semantics implicit in a collection of tagged data. In this paper, we innovate and investigate techniques to make the implicit semantics explicit, so that search can be improved in both precision and recall and additional utility can be derived from the tags that people associate with multimedia items (pictures, blogs, videos, etc.). Our approach is to propose hypotheses about the ontological structure inherent in a collection of tags and then attempt to verify the hypotheses statistically. We conducted more than one hundred experimental searches on Flickr with different tags and discovered by statistical analysis information about how tags are assigned by users and what ontological knowledge is implicit in these tags that can be made explicit, and ultimately, exploited.},
  address = {New York, NY, USA},
  author = {Lalwani, Saurabh and Huhns, Michael N.},
  booktitle = {ACM-SE 47: Proceedings of the 47th Annual Southeast Regional Conference},
  doi = {http://doi.acm.org/10.1145/1566445.1566512},
  file = {lalwani2009deriving.pdf:lalwani2009deriving.pdf:PDF},
  groups = {public},
  interhash = {84ca9a71836a3475f601f6522e1d4da1},
  intrahash = {5d2c6d723aa835c4f245723200f93173},
  isbn = {978-1-60558-421-8},
  location = {Clemson, South Carolina},
  pages = {1--2},
  publisher = {ACM},
  timestamp = {2010-01-18 20:11:04},
  title = {Deriving ontological structure from a folksonomy},
  url = {http://portal.acm.org/citation.cfm?id=1566445.1566512},
  username = {dbenz},
  year = 2009
}

@inproceedings{liu2008between,
  abstract = {We present our first user study of CRAFT, a semantic prototype for collaborative investigation and analysis, which allows users to extend the system's ontology to capture new concepts as they conduct their work. We devised a paradigm in which multiple series of ontologies evolve in different trajectories from the same initial point. We analyze the ontology evolution quantitatively with several metrics, and user behavior qualitatively through interviews and observation. Based on our study, we propose a set of design suggestions for semantic applications with collaborative and implicit ontology development.},
  address = {New York, NY, USA},
  author = {Liu, Jiahui and Gruen, Daniel M.},
  booktitle = {IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/1378773.1378830},
  file = {liu2008between.pdf:liu2008between.pdf:PDF},
  groups = {public},
  interhash = {45e8362f274501ec6b2a81e2693b405e},
  intrahash = {8c92fc47200127eeb84f2964a3d1b528},
  isbn = {978-1-59593-987-6},
  location = {Gran Canaria, Spain},
  pages = {361--364},
  publisher = {ACM},
  timestamp = {2010-01-18 20:13:25},
  title = {Between ontology and folksonomy: a study of collaborative and implicit ontology evolution},
  url = {http://portal.acm.org/citation.cfm?id=1378830},
  username = {dbenz},
  year = 2008
}

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

@inproceedings{ramage2009clustering,
  abstract = {Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically significant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase.},
  address = {New York, NY, USA},
  author = {Ramage, Daniel and Heymann, Paul and Manning, Christopher D. and Garcia-Molina, Hector},
  booktitle = {WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining},
  doi = {http://doi.acm.org/10.1145/1498759.1498809},
  file = {ramage2009clustering.pdf:ramage2009clustering.pdf:PDF},
  groups = {public},
  interhash = {5595f06f88310ed67fd6fe23f813c69b},
  intrahash = {75c4bad29d7eb4b34f68da27f0353516},
  isbn = {978-1-60558-390-7},
  location = {Barcelona, Spain},
  pages = {54--63},
  publisher = {ACM},
  timestamp = {2009-04-24 10:19:45},
  title = {Clustering the tagged web},
  url = {http://portal.acm.org/citation.cfm?id=1498809},
  username = {dbenz},
  year = 2009
}

@inproceedings{tang2009towards,
  abstract = {A folksonomy refers to a collection of user-defined tags with which users describe contents published on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.},
  address = {San Francisco, CA, USA},
  author = {Tang, Jie and fung Leung, Ho and Luo, Qiong and Chen, Dewei and Gong, Jibin},
  booktitle = {IJCAI'09: Proceedings of the 21st international jont conference on Artifical intelligence},
  file = {tang2009towards.pdf:tang2009towards.pdf:PDF},
  groups = {public},
  interhash = {17f95a6ba585888cf45443926d8b7e98},
  intrahash = {7b335f08a288a79eb70eff89f1ec7630},
  location = {Pasadena, California, USA},
  pages = {2089--2094},
  publisher = {Morgan Kaufmann Publishers Inc.},
  timestamp = {2009-12-23 21:30:44},
  title = {Towards ontology learning from folksonomies},
  url = {http://ijcai.org/papers09/Papers/IJCAI09-344.pdf},
  username = {dbenz},
  year = 2009
}