http://www.bibsonomy.org/burst/user/nepomuk/myownBibSonomy RSS feed for /user/nepomuk/myown2010-03-20T09:19:42+01:00http://www.bibsonomy.org/bibtex/27eb26a177187ea8cf788cc897d66ee48/nepomuknepomuk2010-02-04T09:21:48+01:002008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 <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>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 style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</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>HT '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 style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em><em>6(1):38--53</em><em>Feb 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 style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> and Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> and Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">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 style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</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>HT '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/2359e1eccdc524334d4a2ad51330f76ae/nepomuknepomuk2008-06-20T14:39:26+02:002008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 <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 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 style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em><em>6(1):38--53</em><em>feb 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 style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Leandro <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivit&#228;t LWA 2007, </em><em>page 13-20. </em><em>Martin-Luther-Universit&#228;t Halle-Wittenberg, </em><em>sep 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/27da79be07a02074b80b321e36d872e5d/nepomuknepomuk2007-11-12T14:36:27+01:00MyOwn from:uvriss knowledge_sharing task_management <span style="color:#555555;">Uwe V. <a href="http://www.bibsonomy.org/author/Riss">Riss</a> and Ulrike <a href="http://www.bibsonomy.org/author/Cress">Cress</a> and Joachim <a href="http://www.bibsonomy.org/author/Kimmerle">Kimmerle</a> and Stefan <a href="http://www.bibsonomy.org/author/Martin">Martin</a> </span><em>Knowledge Management Research and Practice</em>(<em>2007</em>)Mon Nov 12 14:36:27 CET 2007Knowledge Management Research and Practice287-296Knowledge transfer by sharing task templates: two approaches and their psychological requirements.52007MyOwn from:uvriss knowledge_sharing task_management This paper considers two paradigms of process-aware information systems (PAIS) that are used to share knowledge about planning and executing tasks. The case-based task management (CBTM) system is based on the transduction of execution protocols, so-called cases; and the pattern-based task management (PBTM) system is based on task patterns that are derived from cases but yield more abstract information. As user motivation to contribute to a collaborative task management system is a crucial factor for its success, we consider the different motivational requirements of CBTM and PBTM from a psychological point of view. Based on experimental findings about different factors that have been shown to enhance people's motivation to contribute their knowledge to a shared pool, we compare the two approaches and assess which one is more appropriate with respect to motivation. Knowledge Management Research & Practice - Abstract of article: Knowledge transfer by sharing task templates: two approaches and their psychological requirementshttp://www.bibsonomy.org/bibtex/2bb8ecec699a2f129322fe334747c6aef/nepomuknepomuk2007-11-12T12:55:47+01:002007 folksonomy from:jaeschke l3s myown recommender tagging wp5 <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Leandro Balby <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">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 style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">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/242cda5911e901eadd0ac6a106a6aa1dc/nepomuknepomuk2007-03-22T18:29:28+01:002006 UniK detection folkrank folksonomy from:pchirita hotho jaeschke l3s myown nepomuk pagerank schmitz stumme tagorapub trend triadic wp5 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. First International Conference on Semantics And Digital Media Technology SAMT, </em><em>volume 4306 of LNCS, </em><em>page 56-70. </em><em>Heidelberg, </em><em>Springer, </em><em>dec 2006. </em>Thu Mar 22 18:29:28 CET 2007HeidelbergProc. First International Conference on Semantics And Digital Media Technology (SAMT) dec56-70LNCSTrend Detection in Folksonomies430620062006 UniK detection folkrank folksonomy from:pchirita hotho jaeschke l3s myown nepomuk pagerank schmitz stumme tagorapub trend triadic wp5 As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents. One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.http://www.bibsonomy.org/bibtex/25854a71547051543dd3d3d5e2e2f2b67/nepomuknepomuk2007-03-22T18:29:28+01:002006 bibsonomy bookmarking folksonomy from:pchirita iccs l3s myown nepomuk social wp5 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, </em><em>page 87-102. </em><em>Aalborg, </em><em>Aalborg Universitetsforlag, </em>(<em>2006</em>)Thu Mar 22 18:29:28 CET 2007AalborgProceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures87-102{BibSonomy}: A Social Bookmark and Publication Sharing System20062006 bibsonomy bookmarking folksonomy from:pchirita iccs l3s myown nepomuk social wp5 Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references in a kind of personal library.http://www.bibsonomy.org/bibtex/211b2a59a568d246d7f36cb68169a464a/nepomuknepomuk2007-03-22T18:29:28+01:002006 analysis folksonomies folksonomy from:pchirita l3s myown nepomuk network semantic wp5 <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Data Science and Classification. Proceedings of the 10th IFCS Conf., </em><em>page 261--270. </em><em>Heidelberg, </em><em>Springer, </em><em>July 2006. </em>Thu Mar 22 18:29:28 CET 2007HeidelbergData Science and Classification. Proceedings of the 10th IFCS Conf.July261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 analysis folksonomies folksonomy from:pchirita l3s myown nepomuk network semantic wp5 Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.http://www.bibsonomy.org/bibtex/205043cc20f1e0f5a612135c970e4f1ac/nepomuknepomuk2007-03-22T18:29:28+01:002006 UniK bibsonomy emergence emergent folksonomy from:pchirita hotho jaeschke l3s myown nepomuk schmitz semantics stumme tagorapub wp5 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Informatik 2006 - Informatik f&#252;r Menschen. Band 2, </em><em>volume P-94 of Lecture Notes in Informatics, </em><em>Bonn, </em><em>Gesellschaft f&#252;r Informatik, </em><em>oct 2006. </em><em>Proc. Workshop on Applications of Semantic Technologies, Informatik 2006 . </em>Thu Mar 22 18:29:28 CET 2007BonnInformatik 2006 - Informatik für Menschen. Band 2octProc. Workshop on Applications of Semantic Technologies, Informatik 2006Lecture Notes in InformaticsEmergent Semantics in BibSonomyP-9420062006 UniK bibsonomy emergence emergent folksonomy from:pchirita hotho jaeschke l3s myown nepomuk schmitz semantics stumme tagorapub wp5 Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.http://www.bibsonomy.org/bibtex/253e13744981f2c04d9239e0cf9b4e689/nepomuknepomuk2007-03-22T18:29:28+01:002006 Wissensmanagement collaborative folksonomy from:pchirita knowledge l3s management myown nepomuk p2p semantic web wissensmanagement wp5 <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>page 273-290. </em><em>Springer, </em>(<em>2006</em>)Thu Mar 22 18:29:28 CET 2007Semantic Web - Wege zur vernetzten Wissensgesellschaft273-290Kollaboratives Wissensmanagement20062006 Wissensmanagement collaborative folksonomy from:pchirita knowledge l3s management myown nepomuk p2p semantic web wissensmanagement wp5 Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.http://www.bibsonomy.org/bibtex/24d8b4f79814691fbe6db8357d63206a1/nepomuknepomuk2007-03-22T18:29:28+01:002006 algorithm folkrank from:pchirita ir l3s myown nepomuk pagerank ranking wp5 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. FGIR 2006, </em><em>page to appear. </em>(<em>2006</em>)Thu Mar 22 18:29:28 CET 2007Proc. FGIR 2006to appearFolkRank: A Ranking Algorithm for Folksonomies20062006 algorithm folkrank from:pchirita ir l3s myown nepomuk pagerank ranking wp5 In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.Short version of http://www.bibsonomy.org/bibtex/07f061a8c294bae64831fbe92dc9ece48/stummehttp://www.bibsonomy.org/bibtex/2c44763991d44182c53606a2c93054f26/nepomuknepomuk2007-03-22T18:29:28+01:002005 analysis from:pchirita iswc l3s myown nepomuk network proceedings semantic semna sna workshop wp5 <span style="color:#555555;">Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> and Bettina <a href="http://www.bibsonomy.org/author/Hoser">Hoser</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Harith <a href="http://www.bibsonomy.org/author/Alani">Alani</a> (eds.). </span><em>Aachen, </em><em>CEUR Proceedings, </em>(<em>2005</em>)Thu Mar 22 18:29:28 CET 2007AachenProceedings of the First Workshop on Semantic Network Analysis 20052005 analysis from:pchirita iswc l3s myown nepomuk network proceedings semantic semna sna workshop wp5 Collocated with the 4th International Semantic Web Conferencehttp://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 style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">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