]> BibSonomy publications for /author/Krause http://www.bibsonomy.org/burst/author/Krause BibSonomy BuRST Feed for /author/Krause 2008-07-27T02:34:09+02:00 A Comparison of Social Bookmarking with Traditional Search http://www.bibsonomy.org/bibtex/2581f40c32a996d6d155ba8758d4c277e/mstrohm mstrohm 2008-07-23T13:12:34+02:00 folksonomy search social-factors social-search
A Comparison of Social Bookmarking with Traditional Search
B. Krause and A. Hotho and G. Stumme Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008 101-113 (2008)
to folksonomy search social-factors social-search by mstrohm on 2008-07-23 13:12:34 ]]> Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search49562008folksonomy search social-factors social-search 2008-07-23 13:12:34.0Social 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. Organizing Publications and Bookmarks in BibSonomy BibSonomy::bibtex::Organizing Publications and Bookmarks in BibSonomyhttp://www.bibsonomy.org/bibtex/2b3a5e9851647ca0a7dfb62f041872504/ishmael1851 ishmael1851 2008-07-13T21:29:12+02:00 2007 BibSonomyZukunftReferat bookmarking folksonomy social system
Organizing Publications and Bookmarks in BibSonomy
Robert Jäschke and Miranda Grahl and Andreas Hotho and Beate Krause and Christoph Schmitz and Gerd Stumme Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007 (2007)
to 2007 BibSonomyZukunftReferat bookmarking folksonomy social system by ishmael1851 on 2008-07-13 21:29:12 ]]> Banff, CanadaWorkshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007Organizing Publications and Bookmarks in BibSonomy20072007 BibSonomyZukunftReferat bookmarking folksonomy social system 2008-07-13 21:29:12.0 Pilotstudie Forschungsrating Soziologie: Vorbereitung, Durchführung, Ergebnisse der Erhebung soziologischer Publikationen http://www.bibsonomy.org/bibtex/2c706c4b873814bfcfb2a97174633a5b4/wdees wdees 2008-07-11T15:53:39+02:00 Literaturdatenbank gelesen publikationspraxis rating soziologie
Pilotstudie Forschungsrating Soziologie: Vorbereitung, Durchführung, Ergebnisse der Erhebung soziologischer Publikationen
Stefan Bärisch and Bernd Hermes and Stefan Jakowatz and Jürgen Krause and Udo Riege and Matthias Stahl and Maximilian Stempfhuber and Maria Zens and Monika Zimmer (2008)
to Literaturdatenbank gelesen publikationspraxis rating soziologie by wdees on 2008-07-11 15:53:39 ]]> BonnGESIS-Arbeitsbericht Nr. 5Pilotstudie Forschungsrating Soziologie: Vorbereitung, Durchführung, Ergebnisse der Erhebung soziologischer Publikationen2008Literaturdatenbank gelesen publikationspraxis rating soziologie 2008-07-11 15:53:39.0 Chaos detection and control in production systems. dblphttp://www.bibsonomy.org/bibtex/277d848ea7177a9df70f23e80c73c7956/dblp dblp 2008-07-07T00:00:00+02:00 dblp
Chaos detection and control in production systems.
Bernd Scholz-Reiter and Michael Freitag APMS 416-423 (1999)
to dblp by dblp on 2008-07-07 00:00:00 ]]> APMSconf/ifip5-7/1999amps416-423IFIP Conference ProceedingsChaos detection and control in production systems.1511999dblp 2008-07-07 00:00:00.0 Logsonomy - social information retrieval with logdata. dblphttp://www.bibsonomy.org/bibtex/28107858d7cdc53d4925787a52d25b14a/dblp dblp 2008-06-30T00:00:00+02:00 dblp
Logsonomy - social information retrieval with logdata.
Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme Hypertext 157-166 (2008)
to dblp by dblp on 2008-06-30 00:00:00 ]]> Hypertextconf/ht/2008157-166Logsonomy - social information retrieval with logdata.2008dblp 2008-06-30 00:00:00.0 Logsonomy - Social Information Retrieval with Logdata http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/nepomuk nepomuk 2008-06-20T14:42:46+02:00 analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5
Logsonomy - Social Information Retrieval with Logdata
Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia 157--166 (2008)
to analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5 by nepomuk on 2008-06-20 14:42:46 ]]> New 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 2008-06-20 14:42:46.0Social 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. Logsonomy - Social Information Retrieval with Logdata http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschke jaeschke 2008-06-20T14:42:46+02:00 analysis engine information l3s logsonomy network retrieval search social wp5
Logsonomy - Social Information Retrieval with Logdata
Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia 157--166 (2008)
to analysis engine information l3s logsonomy network retrieval search social wp5 by jaeschke on 2008-06-20 14:42:46 ]]> New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008analysis engine information l3s logsonomy network retrieval search social wp5 2008-06-20 14:42:46.0Social 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. Logsonomy — A Search Engine Folksonomy http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/nepomuk nepomuk 2008-06-20T14:39:26+02:00 2008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5
Logsonomy — A Search Engine Folksonomy
Robert Jäschke and Beate Krause and Andreas Hotho and Gerd Stumme Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) (2008)
to 2008 engine folksonomy from:jaeschke l3s logsonomy myown search wp5 by nepomuk on 2008-06-20 14:39:26 ]]> Proceedings 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 2008-06-20 14:39:26.0In 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. Logsonomy — A Search Engine Folksonomy http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/jaeschke jaeschke 2008-06-20T14:39:26+02:00 2008 engine folksonomy l3s logsonomy myown search wp5
Logsonomy — A Search Engine Folksonomy
Robert Jäschke and Beate Krause and Andreas Hotho and Gerd Stumme Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) (2008)
to 2008 engine folksonomy l3s logsonomy myown search wp5 by jaeschke on 2008-06-20 14:39:26 ]]> Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy20082008 engine folksonomy l3s logsonomy myown search wp5 2008-06-20 14:39:26.0In 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. Case Study on social software in distributed working environments http://www.bibsonomy.org/bibtex/2233a2cfa59bd2512ad39efe7933588fc/yish yish 2008-06-18T13:05:23+02:00 TLRPWeb20Commentary bibsonomy case casestudy patternlanguagenetworks prolearn social socialsoftware software study web2.0
Case Study on social software in distributed working environments
Barbara Kieslinger and Margit Hofer and Yiwei Cao and Karsten Ehms and Sebastian Fiedler and Anna-Kaarina Kairamo and Ralf Klamma and Beate Krause and Milos Kravcik and Tommi Ryyppö and Marc Spaniol and Gerd Stumme and Fridolin Wild (2007)
to TLRPWeb20Commentary bibsonomy case casestudy patternlanguagenetworks prolearn social socialsoftware software study web2.0 by yish on 2008-06-18 13:05:23 ]]> Case Study on social software in distributed working environments2007TLRPWeb20Commentary bibsonomy case casestudy patternlanguagenetworks prolearn social socialsoftware software study web2.0 2008-06-18 13:05:23.0This document outlines four different case studies on the use of Social Software in distributed working environments. While two case studies focus on the corporate world, two other case studies look at the use of Social Software in academic workplaces. The concluding remarks identify some common benefits as well as issues with the use of Social Software and to derive some further research challenges from the different cases. The findings for future research need to be directed towards strategies for corporate learning and working environments to effectively integrate Social Software solutions for very specific needs in different institutional cultures. Therefore the continuation of social software applications in practice, including some other case studies from non-corporate or non-academic, will be essential for WP 15.