http://www.bibsonomy.org/burst/group/kdeBibSonomy BuRST Feed for /group/kde2008-07-21T00:34:44+02:00WWW: WWW '08, Trust-based recommendation systems: an ...http://www.bibsonomy.org/bibtex/2df1446a1756ae4ae4f5556fb635167f2/beatebeate2008-07-20T05:55:30+02:00recommendations reputation networks trust <span style="color:#555555;">Reid <a href="http://www.bibsonomy.org/author/Andersen">Andersen</a> and Christian <a href="http://www.bibsonomy.org/author/Borgs">Borgs</a> and Jennifer <a href="http://www.bibsonomy.org/author/Chayes">Chayes</a> and Uriel <a href="http://www.bibsonomy.org/author/Feige">Feige</a> and Abraham <a href="http://www.bibsonomy.org/author/Flaxman">Flaxman</a> and Adam <a href="http://www.bibsonomy.org/author/Kalai">Kalai</a> and Vahab <a href="http://www.bibsonomy.org/author/Mirrokni">Mirrokni</a> and Moshe <a href="http://www.bibsonomy.org/author/Tennenholtz">Tennenholtz</a> </span><em>WWW '08: Proceeding of the 17th international conference on World Wide Web, </em><em>page199--208. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Sun Jul 20 05:55:30 CEST 2008New York, NY, USAWWW '08: Proceeding of the 17th international conference on World Wide Web199--208Trust-based recommendation systems: an axiomatic approach2008recommendations reputation networks trust http://www.bibsonomy.org/bibtex/2f1a145a60c3e4d39e91b39a7c1178110/dbenzdbenz2008-07-17T10:36:27+02:00folksonomy relations social-networks toread resources extract <span style="color:#555555;">Junichiro <a href="http://www.bibsonomy.org/author/Mori">Mori</a> and Takumi <a href="http://www.bibsonomy.org/author/Tsujishita">Tsujishita</a> and Yutaka <a href="http://www.bibsonomy.org/author/Matsuo">Matsuo</a> and Mitsuru <a href="http://www.bibsonomy.org/author/Ishizuka">Ishizuka</a> </span><em>International Semantic Web Conference, </em><em>page487-500. </em>(<em>2006</em>)Thu Jul 17 10:36:27 CEST 2008International Semantic Web ConferenceDBLP:conf/semweb/2006487-500Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts2006folksonomy relations social-networks toread resources extract http://www.bibsonomy.org/bibtex/2f1a145a60c3e4d39e91b39a7c1178110/beatebeate2008-07-17T00:11:22+02:00social-networks semantic-web relation ie <span style="color:#555555;">Junichiro <a href="http://www.bibsonomy.org/author/Mori">Mori</a> and Takumi <a href="http://www.bibsonomy.org/author/Tsujishita">Tsujishita</a> and Yutaka <a href="http://www.bibsonomy.org/author/Matsuo">Matsuo</a> and Mitsuru <a href="http://www.bibsonomy.org/author/Ishizuka">Ishizuka</a> </span><em>International Semantic Web Conference, </em><em>page487-500. </em>(<em>2006</em>)Thu Jul 17 00:11:22 CEST 2008International Semantic Web ConferenceDBLP:conf/semweb/2006487-500Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts2006social-networks semantic-web relation ie [0710.2889] An efficient reduction of ranking to classificationhttp://www.bibsonomy.org/bibtex/2d8bd1b99e3c245d17b577514727ebff2/hothohotho2008-07-15T18:12:46+02:00ranking toread learning <span style="color:#555555;">Nir <a href="http://www.bibsonomy.org/author/Ailon">Ailon</a> and Mehryar <a href="http://www.bibsonomy.org/author/Mohri">Mohri</a> </span>(<em>2007</em>)Tue Jul 15 18:12:46 CEST 2008An efficient reduction of ranking to classification2007ranking toread learning This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most that of the binary classifier regret, improving a recent result of Balcan et al which only guarantees a factor of 2. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simpler proof, and the expected running time complexity of our algorithm in terms of number of calls to a classifier or preference function is improved from $\Omega(n^2)$ to $O(n \log n)$. In addition, when the top $k$ ranked elements only are required ($k \ll n$), as in many applications in information extraction or search engines, the time complexity of our algorithm can be further reduced to $O(k \log k + n)$. Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied.SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2522c68b8bb5e28f1bf9f1e11e612f542/jaeschkejaeschke2008-07-14T15:41:59+02:00mining itemset algorithm parallel fca set frequent <span style="color:#555555;">Salvatore <a href="http://www.bibsonomy.org/author/Orlando">Orlando</a> and Paolo <a href="http://www.bibsonomy.org/author/Palmerini">Palmerini</a> and Raffaele <a href="http://www.bibsonomy.org/author/Perego">Perego</a> and Fabrizio <a href="http://www.bibsonomy.org/author/Silvestri">Silvestri</a> </span><em>High Performance Computing for Computational Science &#8212; VECPAR 2002, </em><em>page3--29. </em>(<em>2003</em>)Mon Jul 14 15:41:59 CEST 2008High Performance Computing for Computational Science — VECPAR 20023--29An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets2003mining itemset algorithm parallel fca set frequent Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.http://www.bibsonomy.org/bibtex/23aff1098bf9828a0c6683f07145d60bb/jaeschkejaeschke2008-07-14T13:37:17+02:00rule association itemset frequent mining fca closed <span style="color:#555555;">Claudio <a href="http://www.bibsonomy.org/author/Lucchese">Lucchese</a> and Salvatore <a href="http://www.bibsonomy.org/author/Orlando">Orlando</a> and Raffaele <a href="http://www.bibsonomy.org/author/Perego">Perego</a> </span><em>IEEE Transactions On Knowledge and Data Engineering</em><em>18(1):21--36</em>(<em>2006</em>)Mon Jul 14 13:37:17 CEST 2008IEEE Transactions On Knowledge and Data Engineering121--36Fast and Memory Efficient Mining of Frequent Closed Itemsets182006rule association itemset frequent mining fca closed http://www.bibsonomy.org/bibtex/2645abd6b3191a2a6e844d7542651ed1c/jaeschkejaeschke2008-07-12T13:03:47+02:00detection community clustering <span style="color:#555555;">Akshay <a href="http://www.bibsonomy.org/author/Java">Java</a> and Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> and 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 13:03:47 CEST 2008WebKDD 2008 Workshop on Web Mining and Web Usage AnalysisAugustTo AppearDetecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies2008detection community clustering http://www.bibsonomy.org/bibtex/2645abd6b3191a2a6e844d7542651ed1c/hothohotho2008-07-12T12:19:07+02:00folksonomy toread detection community clusterig <span style="color:#555555;">Akshay <a href="http://www.bibsonomy.org/author/Java">Java</a> and Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> and 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}2008folksonomy toread detection community clusterig http://www.bibsonomy.org/bibtex/2573ac0e71d6b1c369cf881ddda8c7841/dbenzdbenz2008-07-07T15:50:15+02:00ontology_learning taxonomic_overlap <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> and Julien <a href="http://www.bibsonomy.org/author/Tane">Tane</a> </span><em>Proceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining, </em><em>page10--17. </em><em>Cavtat-Dubrovnik, Croatia, </em>(<em>2003</em>)Mon Jul 07 15:50:15 CEST 2008Cavtat-Dubrovnik, CroatiaProceedings of the ECML / PKDD Workshop on Adaptive Text Extraction and Mining10--17Automatic Acquisition of Taxonomies from Text: FCA meets NLP2003ontology_learning taxonomic_overlap http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/stummestumme2008-07-04T15:05:56+02:00bookmarking social folksonomy <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>page26-38. </em><em>Oldenbourg Verlag, </em><em>M&#252;nchen, </em>(<em>2008</em>)Fri Jul 04 15:05:56 CEST 2008MünchenWeb 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software26-38Social Bookmarking2008bookmarking social 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 * 2.0 folksonomy ranking web 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>page208-212. </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 * 2.0 folksonomy ranking web 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.D5 MPI-INF Publications: Proceedings Article: Learning Word-to-Concept Mappings for Automatic Text Classificationhttp://www.bibsonomy.org/bibtex/257f8241941ed979455c3dbb90893020f/hothohotho2008-07-01T15:19:39+02:00model text concept wordnet topic tc classification <span style="color:#555555;">Georgiana <a href="http://www.bibsonomy.org/author/Ifrim">Ifrim</a> and Martin <a href="http://www.bibsonomy.org/author/Theobald">Theobald</a> and Gerhard <a href="http://www.bibsonomy.org/author/Weikum">Weikum</a> </span><em>Proceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005), </em><em>page18--26. </em><em>Bonn, Germany, </em>(<em>2005</em>)Tue Jul 01 15:19:39 CEST 2008Bonn, GermanyProceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005)18--26Learning Word-to-Concept Mappings for Automatic Text Classification2005model text concept wordnet topic tc classification http://www.bibsonomy.org/bibtex/2e0743ca2e566e0a2745884f9a2189dbd/stummestumme2008-07-01T09:41:23+02:00world smallworld small <span style="color:#555555;">Brian <a href="http://www.bibsonomy.org/author/Uzzi">Uzzi</a> and Jarett <a href="http://www.bibsonomy.org/author/Spiro">Spiro</a> </span><em>American Journal of Sociology</em><em>111(2):447--504</em>(<em>2005</em>)Tue Jul 01 09:41:23 CEST 2008American Journal of Sociology2447--504Collaboration and Creativity: The Small World Problem1112005world smallworld small Small world networks have received disproportionate notice in diverse fields because of their suspected effect on system dynamics. The authors analyzed the small world network of the creative artists who made Broadway musicals from 1945 to 1989. Using original arguments, new statistical methods, and tests of construct validity, they found that the varying “small world” properties of the systemic level network of these artists affected their creativity in terms of the financial and artistic performance of the musicals they produced. The small world network effect was parabolic; performance increased up to a threshold, after which point the positive effects reversed.http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/hothohotho2008-06-25T10:36:15+02:00myown bookmarking folksonomy 2008 social <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>page26-38. </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 Bookmarking2008myown bookmarking folksonomy 2008 social TRIAS - An Algorithm for Mining Iceberg Tri-Latticeshttp://www.bibsonomy.org/bibtex/2797d40e05a48f4343d7695dac87b5870/jaeschkejaeschke2008-06-23T12:23:48+02:00myown trias 2006 <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>ICDM '06: Proceedings of the Sixth International Conference on Data Mining, </em><em>page907--911. </em><em>Washington, DC, USA, </em><em>IEEE Computer Society, </em>(<em>2006</em>)Mon Jun 23 12:23:48 CEST 2008Washington, DC, USAICDM '06: Proceedings of the Sixth International Conference on Data Mining907--911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices2006myown trias 2006 In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschkejaeschke2008-06-20T14:42:46+02:00for:nepomuk l3s social information engine search retrieval network analysis wp5 logsonomy <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>page157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Fri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008for:nepomuk l3s social information engine search retrieval network analysis wp5 logsonomy Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/jaeschkejaeschke2008-06-20T14:39:26+02:00search 2008 l3s logsonomy folksonomy myown engine wp5 for:nepomuk <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>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 Folksonomy2008search 2008 l3s logsonomy folksonomy myown engine wp5 for:nepomuk 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.Bubble sorthttp://www.bibsonomy.org/bibtex/268e8985978a6c6abd57b7bbef5740d59/jaeschkejaeschke2008-06-18T16:18:15+02:00history sorting bubblesort algorithm <span style="color:#555555;">Owen <a href="http://www.bibsonomy.org/author/Astrachan">Astrachan</a> </span><em>SIGCSE Bull.</em><em>35(1):1--5</em>(<em>2003</em>)Wed Jun 18 16:18:15 CEST 2008New York, NY, USASIGCSE Bull.11--5Bubble sort: an archaeological algorithmic analysis352003history sorting bubblesort algorithm Text books, including books for general audiences, invariably mention bubble sort in discussions of elementary sorting algorithms. We trace the history of bubble sort, its popularity, and its endurance in the face of pedagogical assertions that code and algorithmic examples used in early courses should be of high quality and adhere to established best practices. This paper is more an historical analysis than a philosophical treatise for the exclusion of bubble sort from books and courses. However, sentiments for exclusion are supported by Knuth [17], "In short, the bubble sort seems to have nothing to recommend it, except a catchy name and the fact that it leads to some interesting theoretical problems." Although bubble sort may not be a best practice sort, perhaps the weight of history is more than enough to compensate and provide for its longevity.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+02:00fca bibsonomy myown formal 2008 folksonomy concept tagging discovering analysis <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>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 Folksonomies62008fca bibsonomy myown formal 2008 folksonomy concept tagging discovering analysis 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+02:00sw myown automatic 2008 evaluation ontology ml <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> and Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> and York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> and 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 Ontologies2008sw myown automatic 2008 evaluation ontology ml