BibSonomyburst/user/nepomuk/from:jaeschkeBibSonomy RSS feed for /user/nepomuk/from:jaeschke2012-02-16T18:06:41+01:00http://www.bibsonomy.org/bibtex/27eb26a177187ea8cf788cc897d66ee48/nepomuknepomuk2010-02-04T09:21:48+01:002008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 <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 Media ICWSM 2008, </em><em>page 192--193. </em><em>Menlo Park, CA, USA, </em><em>AAAI Press, </em>(<em>2008</em>)Thu Feb 04 09:21:48 CET 2010Menlo Park, CA, USAProceedings of the Second International Conference on Weblogs and Social Media (ICWSM 2008)192--193Logsonomy -- A Search Engine Folksonomy20082008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 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/2e64d14f3207766f4afc65983fa759ffe/nepomuknepomuk2009-05-13T09:36:02+02:002008 analysis engine from:jaeschke information l3s logsonomy myown network retrieval search social wp5 <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 May 13 09:36:02 CEST 2009New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082008 analysis engine from:jaeschke information l3s logsonomy myown network retrieval search social wp5 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/218e8babe208fae2c0342438617b0ec31/nepomuknepomuk2009-01-05T13:51:02+01:002008 analysis concept folksonomy formal from:jaeschke l3s myown tagging trias wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>6(1):38--53</em> (<em>February 2008</em>)Mon Jan 05 13:51:02 CET 2009New YorkSemantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide WebFeb138--53Discovering Shared Conceptualizations in Folksonomies620082008 analysis concept folksonomy formal from:jaeschke l3s myown tagging trias wp5 Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/25ccf05a86e7f1a089ae83dd47568e6de/nepomuknepomuk2008-10-13T10:42:29+02:002008 bibsonomy bookmarking from:jaeschke l3s myown social <span class="authorEditorList"><a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Benz">Dominik Benz</a>, <a href="/author/Grahl">Miranda Grahl</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Social Semantic Web, </em><em>chapter 18, </em><em>Springer, </em><em>Berlin, Heidelberg, </em>(<em>2009</em>)Mon Oct 13 10:42:29 CEST 2008Berlin, HeidelbergSocial Semantic Web18363--391X.media.pressSocial Bookmarking am Beispiel BibSonomy20092008 bibsonomy bookmarking from:jaeschke l3s myown social BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der inBibSonomy und ähnlichen Systemen enthaltenen Daten ein.SpringerLink - Buchkapitelhttp://www.bibsonomy.org/bibtex/2c7f43f2f922de1e7febedd10347e80cb/nepomuknepomuk2008-10-08T16:12:30+02:002008 from:jaeschke l3s logsonomy myown <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 Oct 08 16:12:30 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - social information retrieval with logdata20082008 from:jaeschke l3s logsonomy myown HT: HT '08, Logsonomy - social information ...http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/nepomuknepomuk2008-06-20T14:42:46+02:00analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5 <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>)Fri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5 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/2359e1eccdc524334d4a2ad51330f76ae/nepomuknepomuk2008-06-20T14:39:26+02:002008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 <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 Jun 20 14:39:26 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy20082008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 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/263901930c137df0c2dad84075c564b14/nepomuknepomuk2008-04-15T11:58:42+02:002008 analysis concept folksonomy formal from:jaeschke l3s myown tagging wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>6(1):38--53</em> (<em>February 2008</em>)Tue Apr 15 11:58:42 CEST 2008Semantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide Webfeb138--53Discovering Shared Conceptualizations in Folksonomies620082008 analysis concept folksonomy formal from:jaeschke l3s myown tagging wp5 Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/nepomuknepomuk2008-01-17T12:54:10+01:002007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging <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>Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität LWA 2007, </em><em>page 13-20. </em><em>Martin-Luther-Universität Halle-Wittenberg, </em>(<em>September 2007</em>)Thu Jan 17 12:54:10 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies20072007 folksonomy from:jaeschke kdml l3s lwa myown recommender tagging 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.http://www.bibsonomy.org/bibtex/2bb8ecec699a2f129322fe334747c6aef/nepomuknepomuk2007-11-12T12:55:47+01:002007 folksonomy from:jaeschke l3s myown recommender tagging wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Marinho">Leandro Balby 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>Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, </em><em>volume 4702 of Lecture Notes in Computer Science, </em><em>page 506-514. </em><em>Berlin, Heidelberg, </em><em>Springer, </em>(<em>2007</em>)Mon Nov 12 12:55:47 CET 2007Berlin, HeidelbergKnowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies470220072007 folksonomy from:jaeschke l3s myown recommender tagging wp5 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 two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.http://www.bibsonomy.org/bibtex/22cbbe0790e81d5af7927ba51098fa126/nepomuknepomuk2007-06-13T16:58:36+02:002007 analysis bibsonomy bookmarking fca folksonomy from:jaeschke iccs myown social trias <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 15th International Conference on Conceptual Structures, </em><em>Sheffield, England, </em>(<em>July 2007</em>)Wed Jun 13 16:58:36 CEST 2007Sheffield, EnglandProceedings of the 15th International Conference on Conceptual StructuresJulyAnalysis of the Publication Sharing Behaviour in {BibSonomy}20072007 analysis bibsonomy bookmarking fca folksonomy from:jaeschke iccs myown social trias BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.http://www.bibsonomy.org/bibtex/2e387c294129e11f4221514d5fa807e26/nepomuknepomuk2007-01-11T13:54:35+01:002006 issi_example triadic trias myown icdm wp5 l3s from:jaeschke algorithm 12 fca <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 6th IEEE International Conference on Data Mining ICDM 06, </em><em>page 907-911. </em><em>Hong Kong, </em><em>IEEE Computer Society, </em>(<em>December 2006</em>)Thu Jan 11 13:54:35 CET 2007Hong KongProceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)December907-911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices20062006 issi_example triadic trias myown icdm wp5 l3s from:jaeschke algorithm 12 fca