http://www.bibsonomy.org/burst/user/stumme/2008BibSonomy BuRST Feed for /user/stumme/20082008-07-26T04:38:16+02:00http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/stummestumme2008-06-09T21:10:50+02:00web2.0 systems bookmarking 2008 tagorapub spam 2.0 myown folksonomy folksonomies social web tagger <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web, </em>(<em>2008</em>)Mon Jun 09 21:10:50 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008web2.0 systems bookmarking 2008 tagorapub spam 2.0 myown folksonomy folksonomies social web tagger http://www.bibsonomy.org/bibtex/23b0aca61b24e4343bd80390614e3066e/stummestumme2008-06-09T14:05:11+02:00myown web2.0 web folksonomy collaborative tagging systems 2008 semantic folksonomies 2.0 <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> and Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3), </em><em>Patras, Greece, </em><em>July2008. </em>Mon Jun 09 14:05:11 CEST 2008Patras, GreeceProceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)JulySemantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems2008myown web2.0 web folksonomy collaborative tagging systems 2008 semantic folksonomies 2.0 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.http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/stummestumme2008-04-15T15:02:22+02:00tagorapub engine myown folksonomy logsonomy logsonomies folksonomies 2008 search <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008), </em><em>AAAI Press, </em>(<em>2008</em>)Tue Apr 15 15:02:22 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy2008tagorapub engine myown folksonomy logsonomy logsonomies folksonomies 2008 search In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.http://www.bibsonomy.org/bibtex/2a412eda2e397f691b5581e0622c50eeb/stummestumme2008-04-15T15:00:17+02:002008 folksonomies search bookmarking tagorapub social comparison folksonomy myown <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, </em><em>page101-113. </em>(<em>2008</em>)Tue Apr 15 15:00:17 CEST 2008Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search20082008 folksonomies search bookmarking tagorapub social comparison folksonomy myown Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.