http://www.bibsonomy.org/burst/user/hotho/myownBibSonomy BuRST Feed for /user/hotho/myown2008-10-16T08:57:41+02:00HT: HT '08, Logsonomy - social information ...http://www.bibsonomy.org/bibtex/2c7f43f2f922de1e7febedd10347e80cb/hothohotho2008-08-28T08:21:19+02:002008 logsonomy web myown folksonomy 2.0 <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 logdata20082008 logsonomy web myown folksonomy 2.0 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+02:002008 mining bookmarking social classification dm myown folksonomy spam ml <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 Systems20082008 mining bookmarking social classification dm myown folksonomy spam ml http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/hothohotho2008-06-25T10:36:15+02:002008 bookmarking social myown folksonomy <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 Bookmarking20082008 bookmarking social myown folksonomy 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+02:002008 formal discovering concept tagging bibsonomy myown folksonomy analysis fca <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 Folksonomies620082008 formal discovering concept tagging bibsonomy myown folksonomy analysis fca 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.Institut AIFB - Publikation: AEON - An Approach to the Automatic Evaluation of Ontologieshttp://www.bibsonomy.org/bibtex/2ea55fe7088ef25cdf060d30d94a09e26/hothohotho2008-06-17T22:30:50+02:002008 automatic myown ontology ml sw evaluation <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> und Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> und York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Journal of Applied Ontology</em>(<em>2008</em>) <em>to appear . </em>Tue Jun 17 22:30:50 CEST 2008Journal of Applied Ontologyto appearAEON - An Approach to the Automatic Evaluation of Ontologies20082008 automatic myown ontology ml sw evaluation dblphttp://www.bibsonomy.org/bibtex/26a076256fc0fbf774cd5e67addc13641/hothohotho2008-06-17T21:04:00+02:00workshop 2007 semantics myown ontology <span style="color:#555555;">Liming <a href="http://www.bibsonomy.org/author/Chen">Chen</a> und Philippe <a href="http://www.bibsonomy.org/author/Cudré-Mauroux">Cudr&#233;-Mauroux</a> und Peter <a href="http://www.bibsonomy.org/author/Haase">Haase</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Ernie <a href="http://www.bibsonomy.org/author/Ong">Ong</a> (eds.). </span><em>Volume292vonCEUR Workshop Proceedings, </em><em>CEUR-WS.org, </em>(<em>2007</em>)Tue Jun 17 21:04:00 CEST 2008ESOECEUR Workshop ProceedingsProceedings of the First International Workshop on Emergent Semantics and Ontology Evolution, ESOE 2007, co-located with ISWC 2007 + ASWC 2007, Busan, Korea, November 12th, 20072922007workshop 2007 semantics myown ontology http://www.bibsonomy.org/bibtex/2f15cc7613101babb2c3ed1927e35213a/hothohotho2008-05-21T10:23:17+02:002007 summerschool myown properties folksonomies sosbuch network kdubiq <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andrea <a href="http://www.bibsonomy.org/author/Baldassarri">Baldassarri</a> und Vito D. P. <a href="http://www.bibsonomy.org/author/Servedio">Servedio</a> und Vittorio <a href="http://www.bibsonomy.org/author/Loreto">Loreto</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>AI Communications</em><em>20(4):245 - 262</em>(<em>2007</em>)Wed May 21 10:23:17 CEST 2008AI Communications4245 - 262Network Properties of Folksonomies2020072007 summerschool myown properties folksonomies sosbuch network kdubiq http://www.bibsonomy.org/bibtex/2613f5c41ff759fc548c9085102d1c933/hothohotho2008-05-19T14:43:47+02:002008 bookmarking search social query myown log <span style="color:#555555;">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>Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, </em><em>4956, </em><em>Seite101-113. </em><em>Springer, </em>(<em>2008</em>)Mon May 19 14:43:47 CEST 2008Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search495620082008 bookmarking search social query myown log 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.[0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systemshttp://www.bibsonomy.org/bibtex/278fd64c3db55e6387ebdeb6c40054542/hothohotho2008-05-19T08:38:59+02:002008 tag similarity semantic myown ol learning ontology analysis <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> und Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</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>2008</em>)Mon May 19 08:38:59 CEST 2008Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems20082008 tag similarity semantic myown ol learning ontology analysis 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/22d7c9ea5484ee45ea8bf3520138d7477/hothohotho2008-04-17T10:10:02+02:00text 2005 myown ol learning SumSchool06 fca <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> und A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>Journal of Artificial Intelligence Research (JAIR)</em>(<em>2005</em>)Thu Apr 17 10:10:02 CEST 2008Journal of Artificial Intelligence Research (JAIR)305-339Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis242005text 2005 myown ol learning SumSchool06 fca http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/hothohotho2008-04-05T17:54:56+02:002008 search query logsonomy myown icwsm folksonomy analysis log 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 Folksonomy20082008 search query logsonomy myown icwsm folksonomy analysis log 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.SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2121bdc75ba2f61db089bac5f715a07ba/hothohotho2008-02-28T10:16:38+01:00text 2006 classification myown ol clustering <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> und Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>From Data and Information Analysis to Knowledge Engineering</em>(<em>2006</em>)Thu Feb 28 10:16:38 CET 2008From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification2006text 2006 classification myown ol clustering Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -http://www.bibsonomy.org/bibtex/292d3a5fdd786086fa12787e3e350b6af/hothohotho2008-02-13T12:26:23+01:00mining ki ir 2007 web myown ml introduction <span style="color:#555555;">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>K&#252;nstliche Intelligenz</em>(<em>2007</em>)Wed Feb 13 12:26:23 CET 2008Künstliche Intelligenz35-8Mining the World Wide Web2007mining ki ir 2007 web myown ml introduction http://www.bibsonomy.org/bibtex/2334d3ab11400c4a3ea3ed5b1e95c1855/hothohotho2008-02-01T09:28:11+01:00bookmarking 2007 tagging social summerschool myown folksonomy sosbuch conceptual kdubiq clustering <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 Sites2007bookmarking 2007 tagging social summerschool myown folksonomy sosbuch conceptual kdubiq clustering 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+01:002007 summerschool myown folksonomy learning ontology kdubiq <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.20072007 summerschool myown folksonomy learning ontology kdubiq http://www.bibsonomy.org/bibtex/2bfc43dfe59f9c0935ac3364b12e6d795/hothohotho2008-01-31T12:24:24+01:00bookmarking 2007 collaborative filtering social recommender myown folksonomy <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 Folksonomies2007bookmarking 2007 collaborative filtering social recommender myown folksonomy http://www.bibsonomy.org/bibtex/26d5188d66564fe4ed7386e28868504de/hothohotho2008-01-31T12:22:52+01:00bookmarking 2007 collaborative social myown folksonomy clustering <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 Sites2007bookmarking 2007 collaborative social myown folksonomy clustering DBLP Record 'conf/pkdd/JaschkeMHSS07'http://www.bibsonomy.org/bibtex/2b8b87c78e9e27a44aacde0402c642bff/hothohotho2008-01-31T12:17:28+01:002007 pagerank tagging social summerschool recommender myown folksonomy 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 Folksonomies470220072007 pagerank tagging social summerschool recommender myown folksonomy sosbuch kdubiq http://www.bibsonomy.org/bibtex/2c5c43ae4a719e6e935a9ca1a4aca906b/hothohotho2008-01-31T12:15:29+01:002007 eswc myown ol learning ontology <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> und Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> und York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Proceedings of the European Semantic Web Conference, ESWC2007, </em><em>Volume4519vonLecture Notes in Computer Science, </em><em>Springer-Verlag, </em><em>July2007. </em>Thu Jan 31 12:15:29 CET 2008Proceedings of the European Semantic Web Conference, ESWC2007JulyLecture Notes in Computer Science{Learning Disjointness}451920072007 eswc myown ol learning ontology http://www.bibsonomy.org/bibtex/289f661a20f44fae705ceccd3619cbced/hothohotho2008-01-31T12:12:22+01:002007 trias bibsonomy myown iccs fca <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 Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 15th International Conference on Conceptual Structures, </em><em>Volume4604vonLNCS, </em><em>Sheffield, England, </em><em>July2007. </em>Thu Jan 31 12:12:22 CET 2008Sheffield, EnglandProceedings of the 15th International Conference on Conceptual StructuresJulyLNCSAnalysis of the Publication Sharing Behaviour in {BibSonomy}460420072007 trias bibsonomy myown iccs fca 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.