%0 
%0 Book
%A Boersch, Ingo; Heinsohn, Jochen & Socher, Rolf
%D 2007
%T Wissensverarbeitung
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%I Spektrum Akademischer Verlag
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%7 2., Aufl.
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%K ai ki knowledge wissensverarbeitung 
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%0 
%0 Generic
%A Cattuto, Ciro; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%D 2008
%T Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems
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%F cattuto-2008
%K master_thesis measure semantic similarity tag tagging 
%X  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.
%Z 
%U http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045
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%0 
%0 Conference Proceedings
%A Cimiano, Philipp; Staab, Steffen & Tane, Julien
%D 2003
%T Automatic Acquisition of Taxonomies from Text: FCA meets NLP
%E 
%B Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining
%C Cavtat-Dubrovnik, Croatia
%I 
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%F cimiano2003automatic
%K evaluation master_thesis ontology_learning taxonomic_overlap 
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%Z 
%U http://www.dcs.shef.ac.uk/~fabio/ATEM03/cimiano-ecml03-atem.pdf
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%0 
%0 Book
%A Coulouris, George; Dollimore, Jean & Kindberg, Tim
%D 2005
%T Verteilte Systeme. Konzepte und Design
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%I Pearson Studium
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%7 3., überarb. A.
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%0 
%0 Report
%A Heymann, Paul & Garcia-Molina, Hector
%D 2006
%T Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems
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%I Computer Science Department
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%F citeulike:739394
%K clustering collaborative folksonomy master_thesis tagging taxonomy 
%X Collaborative tagging systems---systems where many casual users annotate objects with free-form strings (tags) of their choosing---have recently emerged as a powerful way to label and organize large collections of data. During our recent investigation into these types of systems, we discovered a simple but remarkably effective algorithm for converting a large corpus of tags annotating objects in a tagging system into a navigable hierarchical taxonomy of tags. We first discuss the algorithm and then present a preliminary model to explain why it is so effective in these types of systems.
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%U http://dbpubs.stanford.edu:8090/pub/2006-10
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Emergent Semantics in BibSonomy
%E Hochberger, Christian & Liskowsky, Rüdiger
%B Informatik 2006 - Informatik für Menschen. Band 2
%C Bonn
%I Gesellschaft für Informatik
%V P-94
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%S Lecture Notes in Informatics
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%F hotho2006emergent
%K emergent folksonomy master_thesis semantics 
%X Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies, briefly describeour own system BibSonomy, which allows for sharing both bookmarks andpublication references, and discuss first steps towards emergent semantics.
%Z Proc. Workshop on Applications of Semantic Technologies, Informatik 2006
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf
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%0 
%0 Conference Proceedings
%A Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Wege zur Entdeckung von Communities in Folksonomies
%E Braß, Stefan & Hinneburg, Alexander
%B Proc. 18. Workshop Grundlagen von Datenbanken
%C Halle-Wittenberg
%I Martin-Luther-Universität 
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%P 80-84
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%F jaeschke2006wege
%K communities folksonomy master_thesis 
%X Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "`Web 2.0"' -- stellenFolksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten undmit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellenein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006wege.pdf
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%0 
%0 Conference Proceedings
%A Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars & Stumme, Gerd
%D 2007
%T Tag Recommendations in Folksonomies
%E Hinneburg, Alexander
%B Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)
%C 
%I Martin-Luther-Universität Halle-Wittenberg
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%P 13-20
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%F jaeschke07tagKdml
%K folksonomy kdml l3s lwa 
%X Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
In this paper we present two tag recommendation algorithms: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, an adaptation of the well-known PageRank algorithm that can cope with undirected triadic hyperedges. We evaluate and compare both algorithms on large-scale real life datasets and show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf
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%A Maedche, Alexander
%D 2002
%T Ontology Learning for the Semantic Web
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%C Boston
%I Kluwer Academic Publishing
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%K master_thesis ontology_learning 
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%A Russell, Stuart J. & Norvig, Peter
%D 2004
%T Künstliche Intelligenz. Ein moderner Ansatz
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%7 2., überarb. A.
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