BibSonomyburst/tag/logsonomiesBibSonomy RSS feed for /tag/logsonomies2012-02-15T23:28:11+01:00http://www.bibsonomy.org/bibtex/2b697a98a7340585594455ee2e81d238a/dbenzdbenz2011-02-17T17:41:39+01:002009 ecml_pkdd ein itegpub logsonomies myown ol_web2.0 similarity_measures taggingsurvey widely_related workshop <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Kumar">G. Praveen Kumar</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 1st Workshop on Explorative Analytics of Information Networks EIN2009, </em><em>Bled, Slovenia, </em>(<em>September 2009</em>)Thu Feb 17 17:41:39 CET 2011Bled, SloveniaProceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)SeptemberCharacterizing Semantic Relatedness of Search Query Terms20092009 ecml_pkdd ein itegpub logsonomies myown ol_web2.0 similarity_measures taggingsurvey widely_related workshop http://www.bibsonomy.org/bibtex/2ad5f4664d1f5b967a0793b4a26a0edbb/dbenzdbenz2011-01-28T11:33:27+01:002008 nopaper bookmarking social logsonomies tagorapub itegpub <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>30th European Conference on IR Research, ECIR 2008, </em><em>volume 4956 of Lecture Notes in Computer Science, </em><em>page 101-113. </em><em>Glasgow, UK, </em><em>Springer, </em>(<em>April 2008</em>)Fri Jan 28 11:33:27 CET 2011Glasgow, UK30th European Conference on IR Research, ECIR 2008April101-113Lecture Notes in Computer ScienceA Comparison of Social Bookmarking with Traditional Search495620082008 nopaper bookmarking social logsonomies tagorapub itegpub http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/dbenzdbenz2011-01-28T11:33:21+01:002008 search engine logsonomy logsonomies folksonomy tagorapub folksonomies itegpub <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social MediaICWSM 2008, </em><em>AAAI Press, </em>(<em>2008</em>)Fri Jan 28 11:33:21 CET 2011Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy -- A Search Engine Folksonomy20082008 search engine logsonomy logsonomies folksonomy tagorapub folksonomies itegpub In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.http://www.bibsonomy.org/bibtex/2955bcf14f3272ba6eaf3dadbef6c0b10/stummestumme2010-06-29T22:44:43+02:002.0 2008 Recommendations bookmarking itegpub logsonomies myown recommendations recommender social systems tag tagorapub tags web web2.0 web20 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Marinho">Leandro Marinho</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>AI Communications</em> <em>21(4):231-247</em> (<em>2008</em>)Tue Jun 29 22:44:43 CEST 2010AmsterdamAI Communications4231-247Tag Recommendations in Social Bookmarking Systems2120082.0 2008 Recommendations bookmarking itegpub logsonomies myown recommendations recommender social systems tag tagorapub tags web web2.0 web20 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 evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. http://www.bibsonomy.org/bibtex/2e64d14f3207766f4afc65983fa759ffe/tgunkeltgunkel2009-11-25T16:04:26+01:00diplom logsonomies <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>HT &#039;08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, </em><em>page 157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Nov 25 16:04:26 CET 2009New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008diplom logsonomies Social bookmarking systems constitute an established part of the Web 2.0. In such 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. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words 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. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/2b697a98a7340585594455ee2e81d238a/tgunkeltgunkel2009-11-25T15:46:26+01:00diplom logsonomies <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Kumar">G. Praveen Kumar</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 1st Workshop on Explorative Analytics of Information Networks EIN2009, </em><em>Bled, Slovenia, </em>(<em>September 2009</em>)Wed Nov 25 15:46:26 CET 2009Bled, SloveniaProceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)SeptemberCharacterizing Semantic Relatedness of Search Query Terms2009diplom logsonomies http://www.bibsonomy.org/bibtex/270539954a20f7d03a1f21764ff62c0ff/stummestumme2009-10-14T15:20:54+02:00folksonomies folksonomy logsonomies queries social tagging <span class="authorEditorList"><a href="/author/Antonellis">Ioannis Antonellis</a>, <a href="/author/Garcia-Molina">Hector Garcia-Molina</a>, and <a href="/author/Karim">Jawed Karim</a> </span><em>WSDM Late Breaking-Results, </em><em>ACM, </em>(<em>2009</em>)Wed Oct 14 15:20:54 CEST 2009WSDM (Late Breaking-Results)conf/wsdm/2009Tagging with Queries: How and Why?2009folksonomies folksonomy logsonomies queries social tagging dblphttp://www.bibsonomy.org/bibtex/2b697a98a7340585594455ee2e81d238a/praveenpraveen2009-10-10T13:33:15+02:002009 WebMining analysis ecml_pkdd ein logsonomies myown query relatedness search semantic similarity_measures workshop <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Kumar">G. Praveen Kumar</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 1st Workshop on Explorative Analytics of Information Networks EIN2009, </em><em>Bled, Slovenia, </em>(<em>September 2009</em>)Sat Oct 10 13:33:15 CEST 2009Bled, SloveniaProceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)SeptemberCharacterizing Semantic Relatedness of Search Query Terms20092009 WebMining analysis ecml_pkdd ein logsonomies myown query relatedness search semantic similarity_measures workshop http://www.bibsonomy.org/bibtex/2b697a98a7340585594455ee2e81d238a/stummestumme2009-09-25T22:01:05+02:002009 ecml l3s logsonomies measures myown pkdd relatedness semantic similarity <span class="authorEditorList"><a href="/author/Benz">Dominik Benz</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Kumar">G. Praveen Kumar</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 1st Workshop on Explorative Analytics of Information Networks EIN2009, </em><em>Bled, Slovenia, </em>(<em>September 2009</em>)Fri Sep 25 22:01:05 CEST 2009Bled, SloveniaProceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)SeptemberCharacterizing Semantic Relatedness of Search Query Terms20092009 ecml l3s logsonomies measures myown pkdd relatedness semantic similarity http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/stummestumme2009-03-02T22:18:19+01:002008 engine folksonomies folksonomy itegpub logsonomies logsonomy myown search tagorapub <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social MediaICWSM 2008, </em><em>AAAI Press, </em>(<em>2008</em>)Mon Mar 02 22:18:19 CET 2009Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy -- A Search Engine Folksonomy20082008 engine folksonomies folksonomy itegpub logsonomies logsonomy myown search tagorapub 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/286dbae2e531c7f68592f5780b46b6a70/stummestumme2008-12-19T09:48:45+01:002008 bookmarking comparison folksonomies folksonomy itegpub logsonomies myown search social tagorapub <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, </em><em>volume 4956 of LNAI, </em><em>page 101-113. </em><em>Heidelberg, </em><em>Springer, </em>(<em>2008</em>)Fri Dec 19 09:48:45 CET 2008HeidelbergAdvances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113LNAIA Comparison of Social Bookmarking with Traditional Search495620082008 bookmarking comparison folksonomies folksonomy itegpub logsonomies myown search social tagorapub 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.