http://www.bibsonomy.org/burst/user/hotho/folksonomyBibSonomy BuRST Feed for /user/hotho/folksonomy2008-10-07T18:52:44+02:00Tagging and searchinghttp://www.bibsonomy.org/bibtex/27e1dc3f52085093cc33d8fe931253b34/hothohotho2008-09-04T12:37:59+02:00toread search comparision engine ir performance folksonomy retrieval <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Jason Morrison">Jason Morrison</a> </span><em>Inf. Process. Manage.</em><em>44(4):1562--1579</em>(<em>2008</em>)Thu Sep 04 12:37:59 CEST 2008Tarrytown, NY, USAInf. Process. Manage.41562--1579Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web442008toread search comparision engine ir performance folksonomy retrieval Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.HT: HT '08, Logsonomy - social information ...http://www.bibsonomy.org/bibtex/2c7f43f2f922de1e7febedd10347e80cb/hothohotho2008-08-28T08:21:19+02:00web 2008 myown logsonomy 2.0 folksonomy <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> und Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und 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>Seite157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Thu Aug 28 08:21:19 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - social information retrieval with logdata2008web 2008 myown logsonomy 2.0 folksonomy 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/26357f535000a383f228f1e8e56ca86ca/hothohotho2008-08-26T08:37:58+02:00spam myown mining 2008 ml folksonomy dm social classification bookmarking <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und 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>)Tue Aug 26 08:37:58 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008spam myown mining 2008 ml folksonomy dm social classification bookmarking http://www.bibsonomy.org/bibtex/2645abd6b3191a2a6e844d7542651ed1c/hothohotho2008-07-12T12:19:07+02:00toread community detection clusterig folksonomy <span style="color:#555555;">Akshay <a href="http://www.bibsonomy.org/author/Java">Java</a> und Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> und Tim <a href="http://www.bibsonomy.org/author/Finin">Finin</a> </span><em>WebKDD 2008 Workshop on Web Mining and Web Usage Analysis, </em><em>August2008. </em><em>To Appear . </em>Sat Jul 12 12:19:07 CEST 2008WebKDD 2008 Workshop on Web Mining and Web Usage AnalysisAugustTo Appear{Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies}2008toread community detection clusterig folksonomy Welcome to IEEE Xplore 2.0: CoolRank: A Social Solution for Ranking Bookmarked Web Resourceshttp://www.bibsonomy.org/bibtex/24671fb1c606e3d7f559bb25d9b20e47d/hothohotho2008-07-02T16:57:41+02:00toread web * ranking 2.0 folksonomy folkrank <span style="color:#555555;">H.S. <a href="http://www.bibsonomy.org/author/Al-Khalifa">Al-Khalifa</a> </span><em>Innovations in Information Technology, 2007. Innovations '07. 4th International Conference on, </em><em>Seite208-212. </em>(<em>2007</em>)Wed Jul 02 16:57:41 CEST 2008Innovations in Information Technology, 2007. Innovations '07. 4th International Conference on208-212CoolRank: A Social Solution for Ranking Bookmarked Web Resources2007toread web * ranking 2.0 folksonomy folkrank Users tag resources for a variety of reasons and using a variety of conventions. The tags that they provide are stored in social bookmarking services, so these services can provide a rich gateway to a wide and interesting quantity of web resources. The cognitive effort that has gone into making these tags has presumably added value to the description of the resource. In this work we utilize the quantitative value of these tags for ranking bookmarked web resources in social bookmarking services. Our proposed solution is called CoolRank, a simple and intuitive model to rank bookmarked web resources in a social bookmarking service, such as del.icio.us. CoolRank makes use of both quantitative information, based on the number of people who have bookmarked a web resource, and subjective information, based on the words people have used in their tags.http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/hothohotho2008-06-25T10:36:15+02:00bookmarking social folksonomy 2008 myown <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Seite26-38. </em><em>Oldenbourg Verlag, </em><em>M&#252;nchen, </em>(<em>2008</em>)Wed Jun 25 10:36:15 CEST 2008MünchenWeb 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software26-38Social Bookmarking2008bookmarking social folksonomy 2008 myown ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/263901930c137df0c2dad84075c564b14/hothohotho2008-06-17T22:31:22+02:00formal fca analysis concept folksonomy tagging myown discovering bibsonomy 2008 <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> und 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>feb2008. </em><em> . </em>Tue Jun 17 22:31:22 CEST 2008Semantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide Webfeb 138--53Discovering Shared Conceptualizations in Folksonomies62008formal fca analysis concept folksonomy tagging myown discovering bibsonomy 2008 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.Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissionshttp://www.bibsonomy.org/bibtex/23a7b2b52257c04b0da0702a820c3f0fc/hothohotho2008-05-29T17:31:25+02:00recommender folksonomy <span style="color:#555555;">Satoshi <a href="http://www.bibsonomy.org/author/Niwa">Niwa</a> und Takuo <a href="http://www.bibsonomy.org/author/Doi">Doi</a> und Shinichi <a href="http://www.bibsonomy.org/author/Honiden">Honiden</a> </span><em>itng</em>(<em>2006</em>)Thu May 29 17:31:25 CEST 2008Los Alamitos, CA, USAitng388-393Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions002006recommender folksonomy http://www.bibsonomy.org/bibtex/242b4c94cff05ccef031235d661a7a77a/hothohotho2008-05-09T12:06:13+02:00detection clustering *** association folksonomy rules community del.icio.us <span style="color:#555555;">Xin <a href="http://www.bibsonomy.org/author/Li">Li</a> und Lei <a href="http://www.bibsonomy.org/author/Guo">Guo</a> und Yihong E. <a href="http://www.bibsonomy.org/author/Zhao">Zhao</a> </span><em>Proceedings of the 17th International World Wide Web Conference, </em><em>Seite675-684. </em><em>ACM, </em>(<em>2008</em>)Fri May 09 12:06:13 CEST 2008Proceedings of the 17th International World Wide Web Conference675-684Tag-based Social Interest Discovery2008detection clustering *** association folksonomy rules community del.icio.us The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the diffi- culty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections. In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/hothohotho2008-04-05T17:54:56+02:00icwsm 2008 search folksonomy analysis logsonomy query log myown network <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und 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>)Sat Apr 05 17:54:56 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy2008icwsm 2008 search folksonomy analysis logsonomy query log myown network 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.Novelty and collective attention -- Wu and Huberman 104 (45): 17599 -- Proceedings of the National Academy of Scienceshttp://www.bibsonomy.org/bibtex/2ff0a7c4758b8bfdf5cf117f652884728/hothohotho2008-03-18T08:14:03+01:00attention collective novelty folksonomy toread <span style="color:#555555;">F. <a href="http://www.bibsonomy.org/author/Wu">Wu</a> und B. A. <a href="http://www.bibsonomy.org/author/Huberman">Huberman</a> </span><em>Proc. Natl. Acad. Sci. USA</em><em>104(45):17599-17601</em>(<em>2007</em>)Tue Mar 18 08:14:03 CET 2008Proc. Natl. Acad. Sci. USA4517599-17601Novelty and collective attention1042007attention collective novelty folksonomy toread The subject of collective attention is central to an information age where millions of people are inundated with daily messages. It is thus of interest to understand how attention to novel items propagates and eventually fades among large populations. We have analyzed the dynamics of collective attention among 1 million users of an interactive web site, digg.com, devoted to thousands of novel news stories. The observations can be described by a dynamical model characterized by a single novelty factor. Our measurements indicate that novelty within groups decays with a stretched-exponential law, suggesting the existence of a natural time scale over which attention fades. http://www.bibsonomy.org/bibtex/2334d3ab11400c4a3ea3ed5b1e95c1855/hothohotho2008-02-01T09:28:11+01:00folksonomy 2007 conceptual tagging summerschool social bookmarking myown clustering sosbuch kdubiq <span style="color:#555555;">Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>7th International Conference on Knowledge Management (I-KNOW '07), </em><em>Seite356-364. </em><em>Graz, Austria, </em><em>Know-Center, </em><em>SEP2007. </em>Fri Feb 01 09:28:11 CET 2008Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)SEP356-364Conceptual Clustering of Social Bookmarking Sites2007folksonomy 2007 conceptual tagging summerschool social bookmarking myown clustering sosbuch kdubiq Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.dblphttp://www.bibsonomy.org/bibtex/2ad31989b2393f5d0c4e8be8dbb613141/hothohotho2008-02-01T09:19:11+01:00ontology kdubiq 2007 folksonomy learning myown summerschool <span style="color:#555555;">Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA), </em><em>Seite109-112. </em><em>Martin-Luther-University Halle-Wittenberg, </em>(<em>2007</em>)Fri Feb 01 09:19:11 CET 2008LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)conf/lwa/2007109-112Position Paper: Ontology Learning from Folksonomies.2007ontology kdubiq 2007 folksonomy learning myown summerschool http://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/hothohotho2008-01-31T12:24:24+01:00social recommender bookmarking myown 2007 folksonomy collaborative filtering <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Leandro <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> und 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>Seite13-20. </em><em>Martin-Luther-Universit&#228;t Halle-Wittenberg, </em><em>sep2007. </em>Thu Jan 31 12:24:24 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep13-20Tag Recommendations in Folksonomies2007social recommender bookmarking myown 2007 folksonomy collaborative filtering http://www.bibsonomy.org/bibtex/26d5188d66564fe4ed7386e28868504de/hothohotho2008-01-31T12:22:52+01:00clustering collaborative myown bookmarking folksonomy social 2007 <span style="color:#555555;">Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und 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>Seite50-54. </em><em>Martin-Luther-Universit&#228;t Halle-Wittenberg, </em><em>sep2007. </em>Thu Jan 31 12:22:52 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites2007clustering collaborative myown bookmarking folksonomy social 2007 DBLP Record 'conf/pkdd/JaschkeMHSS07'http://www.bibsonomy.org/bibtex/2b8b87c78e9e27a44aacde0402c642bff/hothohotho2008-01-31T12:17:28+01:00tagging recommender myown 2007 summerschool folksonomy social pagerank sosbuch kdubiq <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Leandro Balby <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> und 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, Warsaw, Poland, September 17-21, 2007, Proceedings, </em><em>Volume4702vonLecture Notes in Computer Science, </em><em>Seite506-514. </em><em>Springer, </em>(<em>2007</em>)Thu Jan 31 12:17:28 CET 2008Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007tagging recommender myown 2007 summerschool folksonomy social pagerank sosbuch kdubiq http://www.bibsonomy.org/bibtex/22fe2537d42bd46b0a31a25c125eb865d/hothohotho2008-01-25T19:51:15+01:00semantic learning summerschool kdubiq ontology folksonomy <span style="color:#555555;">Francis <a href="http://www.bibsonomy.org/author/Heylighen">Heylighen</a> </span><em>Kybernetes</em><em>30(5/6):691--722</em>(<em>2001</em>)Fri Jan 25 19:51:15 CET 2008Kybernetes5/6691--722Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs302001semantic learning summerschool kdubiq ontology folksonomy Using PersonalizedWeb Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systemshttp://www.bibsonomy.org/bibtex/2799817443dab31b534315a790c24a9f6/hothohotho2008-01-25T13:58:35+01:00kdubiq ranking search summerschool folksonomy <span style="color:#555555;">Mohammad <a href="http://www.bibsonomy.org/author/Nauman">Nauman</a> und Shahbaz <a href="http://www.bibsonomy.org/author/Khan">Khan</a> </span><em>wi</em>(<em>2007</em>)Fri Jan 25 13:58:35 CET 2008Los Alamitos, CA, USAwi423-426Using PersonalizedWeb Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systems02007kdubiq ranking search summerschool folksonomy http://www.bibsonomy.org/bibtex/2539fe40eb8dd2597956cae27d6fb02ac/hothohotho2008-01-25T13:27:16+01:00search tagging kdubiq summerschool cloud tag folksonomy ranking <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Sinclair">Sinclair</a> und M. <a href="http://www.bibsonomy.org/author/Cardew-Hall">Cardew-Hall</a> </span><em>Journal of Information Science</em>(<em>2007</em>)Fri Jan 25 13:27:16 CET 2008Journal of Information Science016555150607808{The folksonomy tag cloud: When is it useful?}2007search tagging kdubiq summerschool cloud tag folksonomy ranking http://www.bibsonomy.org/bibtex/2c8dbb6371be8d67e3aa1928bd3dd0fed/hothohotho2008-01-15T10:57:12+01:00myown folksonomy network seminar2006 kdubiq 2006 summerschool analysis semantic rules sosbuch association <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Data Science and Classification (Proc. IFCS 2006 Conference), </em><em>Seite261-270. </em><em>Ljubljana, </em><em>Springer, </em><em>July2006. </em>Tue Jan 15 10:57:12 CET 2008LjubljanaData Science and Classification (Proc. IFCS 2006 Conference)July261-270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies2006myown folksonomy network seminar2006 kdubiq 2006 summerschool analysis semantic rules sosbuch association