http://www.bibsonomy.org/burst/tag/proj:bkBibSonomy BuRST Feed for /tag/proj:bk2008-08-21T12:56:13+02:00Discovering interesting usage patterns in text collectionshttp://www.bibsonomy.org/bibtex/28044b4032d5f297b2e08d9c32db08625/wnpxrzwnpxrz2008-05-19T17:54:45+02:00usage proj:cpf imported proj:bk proj:gtf text collection pattern <span style="color:#555555;">Anthony <a href="http://www.bibsonomy.org/author/Don">Don</a> and Elena <a href="http://www.bibsonomy.org/author/Zheleva">Zheleva</a> and Machon <a href="http://www.bibsonomy.org/author/Gregory">Gregory</a> and Sureyya <a href="http://www.bibsonomy.org/author/Tarkan">Tarkan</a> and Loretta <a href="http://www.bibsonomy.org/author/Auvil">Auvil</a> and Tanya <a href="http://www.bibsonomy.org/author/Clement">Clement</a> and Ben <a href="http://www.bibsonomy.org/author/Shneiderman">Shneiderman</a> and Catherine <a href="http://www.bibsonomy.org/author/Plaisant">Plaisant</a> </span><em>CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, </em><em>page213--222. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Mon May 19 17:54:45 CEST 2008New York, NY, USACIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management213--222Discovering interesting usage patterns in text collections: integrating text mining with visualization2007usage proj:cpf imported proj:bk proj:gtf text collection pattern Ontology-focused crawling of Web documentshttp://www.bibsonomy.org/bibtex/29d9bcba93b086195f41402c83da3ff07/wnpxrzwnpxrz2008-04-03T16:50:06+02:00ontology proj:bk proj:gtf crawling web <span style="color:#555555;">Marc <a href="http://www.bibsonomy.org/author/Ehrig">Ehrig</a> and Alexander <a href="http://www.bibsonomy.org/author/Maedche">Maedche</a> </span><em>SAC '03: Proceedings of the 2003 ACM symposium on Applied computing, </em><em>page1174--1178. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2003</em>)Thu Apr 03 16:50:06 CEST 2008New York, NY, USASAC '03: Proceedings of the 2003 ACM symposium on Applied computing1174--1178Ontology-focused crawling of Web documents2003ontology proj:bk proj:gtf crawling web The Web, the largest unstructured database of the world, has greatly improved access to documents. However, documents on the Web are largely disorganized. Due to the distributed nature of the World Wide Web it is difficult to use it as a tool for information and knowledge management. Therefore, users doing the difficult task of exploring the Web have to be supported by intelligent means.This paper proposes an approach for document discovery building on a comprehensive framework for ontology-focused crawling of Web documents. Our framework includes means for using a complex ontology and associated instance elements. It defines several relevance computation strategies and provides an empirical evaluation which has shown promising results.focuses on bipartite / affiliation networkshttp://www.bibsonomy.org/bibtex/250103469c4e839b6f05a522eaacaa3a8/wnpxrzwnpxrz2008-04-03T16:47:59+02:00network proj:bk sna analysis social <span style="color:#555555;">L.C. <a href="http://www.bibsonomy.org/author/Freeman">Freeman</a> and D.R. <a href="http://www.bibsonomy.org/author/White">White</a> </span><em>Sociological Methodology</em>(<em>1993</em>)Thu Apr 03 16:47:59 CEST 2008Sociological Methodology127--146{Using Galois Lattices to Represent Network Data}231993network proj:bk sna analysis social Novelty and collective attention -- Wu and Huberman 104 (45): 17599 -- Proceedings of the National Academy of Scienceshttp://www.bibsonomy.org/bibtex/2395472c69e2f3c239d1d69817d23e894/wnpxrzwnpxrz2008-01-25T17:02:46+01:00proj:bk imported collective novelty attention <span style="color:#555555;">Fang <a href="http://www.bibsonomy.org/author/Wu">Wu</a> and Bernardo A. <a href="http://www.bibsonomy.org/author/Huberman">Huberman</a> </span><em>Proceedings of the National Academy of Sciences</em><em>104(45):17599-17601</em>(<em>2007</em>)Fri Jan 25 17:02:46 CET 2008Proceedings of the National Academy of Sciences4517599-17601{Novelty and collective attention}1042007proj:bk imported collective novelty attention 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. Welcome to IEEE Xplore 2.0: Clustering of the self-organizing maphttp://www.bibsonomy.org/bibtex/24a71cf8fe087584791a58c2c1ce52c98/wnpxrzwnpxrz2008-01-12T11:33:37+01:00proj:bk imported <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Vesanto">Vesanto</a> and E. <a href="http://www.bibsonomy.org/author/Alhoniemi">Alhoniemi</a> </span>(<em>2000</em>)Sat Jan 12 11:33:37 CET 2008Neural Networks, IEEE Transactions on586-600Clustering of the self-organizing map112000proj:bk imported The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation timeCiteULike: Improving Tag-Clouds as Visual Information Retrieval Interfaceshttp://www.bibsonomy.org/bibtex/206f68f9fe46dc6d0f646d932e428dec9/wnpxrzwnpxrz2007-12-03T17:37:36+01:00proj:bk tag tagging visualization ir proj:et cloid <span style="color:#555555;">Y. <a href="http://www.bibsonomy.org/author/Hassan-Montero">Hassan-Montero</a> and V. <a href="http://www.bibsonomy.org/author/Herrero-Solana">Herrero-Solana</a> </span><em>InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies, </em>(<em>2006</em>)Mon Dec 03 17:37:36 CET 2007InScit2006: International Conference on Multidisciplinary Information Sciences and TechnologiesImproving Tag-Clouds as Visual Information Retrieval Interfaces2006proj:bk tag tagging visualization ir proj:et cloid Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.http://www.bibsonomy.org/bibtex/2a7a6cdb6e0790b276d7f0642991e734e/wnpxrzwnpxrz2007-11-21T16:13:12+01:00usage proj:bk personalization mining use profile datamining proj:mh <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Mobasher">Mobasher</a> and R. <a href="http://www.bibsonomy.org/author/Cooley">Cooley</a> and J. <a href="http://www.bibsonomy.org/author/Srivastava">Srivastava</a> </span><em>Communications of the ACM</em><em>43(8):142--151</em>(<em>2000</em>)Wed Nov 21 16:13:12 CET 2007Communications of the ACM8142--151Automatic personalization based on Web usage mining432000usage proj:bk personalization mining use profile datamining proj:mh http://www.bibsonomy.org/bibtex/2dc941da0f5c7da937269241b0df0b3b3/wnpxrzwnpxrz2007-11-21T16:12:33+01:00use web proj:bk profile proj:mh profiling usage mining <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Srivastava">Srivastava</a> and R. <a href="http://www.bibsonomy.org/author/Cooley">Cooley</a> and M. <a href="http://www.bibsonomy.org/author/Deshpande">Deshpande</a> and P.-N. <a href="http://www.bibsonomy.org/author/Tan">Tan</a> </span><em>SIGKDD Explorations</em><em>1(2):12--23</em>(<em>2000</em>)Wed Nov 21 16:12:33 CET 2007SIGKDD Explorations212--23Web usage mining: discovery and application of usage patterns from web data12000use web proj:bk profile proj:mh profiling usage mining http://www.bibsonomy.org/bibtex/23e4e2899e7d6988218d02a264bcfe24a/wnpxrzwnpxrz2007-11-21T16:11:50+01:00probabilistic web toread proj:bk modeling <span style="color:#555555;">Pierre <a href="http://www.bibsonomy.org/author/Baldi">Baldi</a> and Paolo <a href="http://www.bibsonomy.org/author/Frasconi">Frasconi</a> and Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>chapter 4, </em><em>Wiley, </em><em>April2003. </em>Wed Nov 21 16:11:50 CET 2007Modeling the Internet and the Web: Probabilistic Methods and Algorithms4AprilModeling the Internet and the Web: Probabilistic Methods and Algorithms2003probabilistic web toread proj:bk modeling Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet. ? Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level. ? Takes a modern approach based on mathematical, probabilistic, and graphical modeling. ? Provides an integrated presentation of theory, examples, exercises and applications. ? Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.http://www.bibsonomy.org/bibtex/218a9697e8ca04f637487e79b6be9cc83/wnpxrzwnpxrz2007-11-21T16:11:24+01:00web proj:mh profiling user profile proj:bk filtering collaborative <span style="color:#555555;">Brij M. <a href="http://www.bibsonomy.org/author/Masand">Masand</a> and Myra <a href="http://www.bibsonomy.org/author/Spiliopoulou">Spiliopoulou</a> (eds.). </span><em>volume1836ofLecture Notes in Computer Science, </em><em>Springer, </em>(<em>2000</em>)Wed Nov 21 16:11:24 CET 2007WEBKDDLecture Notes in Computer ScienceWeb Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers18362000web proj:mh profiling user profile proj:bk filtering collaborative http://www.bibsonomy.org/bibtex/2d6b7ee7b2b4bdc9c17cf2309d4aaab73/wnpxrzwnpxrz2007-11-21T16:10:49+01:00web proj:bk datamining mining semanticweb <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc Int. Semantic Web Conference, </em><em>Sardinia, Italy, </em>(<em>2002</em>)Wed Nov 21 16:10:49 CET 2007Sardinia, ItalyProc Int. Semantic Web ConferenceTowards Semantic Web Mining.2002web proj:bk datamining mining semanticweb http://www.bibsonomy.org/bibtex/20cef0d9bbd975fc454cc90e862022503/wnpxrzwnpxrz2007-11-21T16:10:23+01:00proj:bk mining datamining web <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Chakrabarti">Chakrabarti</a> </span>(<em>2002</em>)Wed Nov 21 16:10:23 CET 2007Mining the Web: Discovering Knowledge from Hypertext Data2002proj:bk mining datamining web http://www.bibsonomy.org/bibtex/2c0cdbeab23ce0fc1bff5e02c99aab012/wnpxrzwnpxrz2007-11-21T16:10:10+01:00proj:bk network ontology semanticweb <span style="color:#555555;">Bettina <a href="http://www.bibsonomy.org/author/Hoser">Hoser</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 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>The Semantic Web: Research and Applications, </em><em>volume4011ofLNAI, </em><em>page514-529. </em><em>Heidelberg, </em><em>Springer, </em><em>June2006. </em>Wed Nov 21 16:10:10 CET 2007HeidelbergThe Semantic Web: Research and ApplicationsJune514-529LNAISemantic Network Analysis of Ontologies40112006proj:bk network ontology semanticweb A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.http://www.bibsonomy.org/bibtex/2937aa34e1766c8eaf6acc065ae9d2c5c/wnpxrzwnpxrz2007-11-21T16:09:52+01:00datamining proj:gtf proj:bk mining semanticweb <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and D. <a href="http://www.bibsonomy.org/author/Mladenic">Mladenic</a> and M. <a href="http://www.bibsonomy.org/author/van Someren">van Someren</a> and M. <a href="http://www.bibsonomy.org/author/Spiliopoulou">Spiliopoulou</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span>(<em>2004</em>)Wed Nov 21 16:09:52 CET 2007Web Mining: From Web to Semantic Web2004datamining proj:gtf proj:bk mining semanticweb Using social psychology to motivate contributions to online communitieshttp://www.bibsonomy.org/bibtex/25255880684d140abed1908b7d6a2cef5/wnpxrzwnpxrz2007-11-19T15:48:48+01:00community proj:bk psychology collaboration <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Beenen">Beenen</a> and K. <a href="http://www.bibsonomy.org/author/Ling">Ling</a> and X. <a href="http://www.bibsonomy.org/author/Wang">Wang</a> and K. <a href="http://www.bibsonomy.org/author/Chang">Chang</a> and D. <a href="http://www.bibsonomy.org/author/Frankowski">Frankowski</a> and P. <a href="http://www.bibsonomy.org/author/Resnick">Resnick</a> and R. E. <a href="http://www.bibsonomy.org/author/Kraut">Kraut</a> </span><em>CSCW '04: Proceedings of the 2004 ACM conference on Computer supported cooperative work, </em><em>page212--221. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2004</em>)Mon Nov 19 15:48:48 CET 2007New York, NY, USACSCW '04: Proceedings of the 2004 ACM conference on Computer supported cooperative work212--221Using social psychology to motivate contributions to online communities2004community proj:bk psychology collaboration Unifying collaborative and content-based filteringhttp://www.bibsonomy.org/bibtex/2b01107296f27777dd05f71d041235d09/wnpxrzwnpxrz2007-11-04T20:35:40+01:00filtering imported collaborative content proj:bk <span style="color:#555555;">Justin <a href="http://www.bibsonomy.org/author/Basilico">Basilico</a> and Thomas <a href="http://www.bibsonomy.org/author/Hofmann">Hofmann</a> </span><em>ICML '04: Proceedings of the twenty-first international conference on Machine learning, </em><em>page9. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2004</em>)Sun Nov 04 20:35:40 CET 2007New York, NY, USAICML '04: Proceedings of the twenty-first international conference on Machine learning9Unifying collaborative and content-based filtering2004filtering imported collaborative content proj:bk Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.Welcome to IEEE Xplore 2.0: A Collaborative Annotation Frameworkhttp://www.bibsonomy.org/bibtex/27fdfd54861d35cf87a7491b9a15083d1/wnpxrzwnpxrz2007-11-04T09:32:36+01:00web imported collaborative annotation proj:bk proj:gtf <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Shevade">Shevade</a> and H. <a href="http://www.bibsonomy.org/author/Sundaram">Sundaram</a> </span><em>Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, </em><em>page1346-1349. </em>(<em>2005</em>)Sun Nov 04 09:32:36 CET 2007Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on1346-1349A Collaborative Annotation Framework2005web imported collaborative annotation proj:bk proj:gtf This paper describes our system that enables members of a social network to collaboratively annotate a shared media collection. The problem is important since online social networks are emerging as conduits for exchange of everyday experiences. Our collaborative annotation system provides personalized recommendations to each user, based on (a) media features, (b) context, (c) commonsensical relationships and (d) linguistic relationships. We also develop novel concept specificity and abstractness/concreteness measures that further adapt the recommendations to the specific concept. Our preliminary user studies indicate that the system performs well and is more useful as compared to standard Web browser recommendation schemesThis work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language.http://www.bibsonomy.org/bibtex/26a18ac2d858663e593bfc7d9516bc124/wnpxrzwnpxrz2007-07-23T20:28:00+02:00machinelearning language imported nlp morphology proj:bk natlang unsupervised morpheme <span style="color:#555555;">Mathias <a href="http://www.bibsonomy.org/author/Creutz">Creutz</a> and Krista <a href="http://www.bibsonomy.org/author/Lagus">Lagus</a> </span>(<em>2005</em>)Mon Jul 23 20:28:00 CEST 2007Inducing the Morphological Lexicon of a Natural Language from Unannotated Text2005machinelearning language imported nlp morphology proj:bk natlang unsupervised morpheme In our research we infer a social network from the chat data by using a few basic heuristics. We then present some preliminary results showing that the inferred social graph may be used to enhance topic identification of a chat room when combined with a latent variable topic model.http://www.bibsonomy.org/bibtex/2dd8cc2fb5a4a0720c6eb24a4409cd4c3/wnpxrzwnpxrz2007-07-23T20:24:37+02:00social mining network chat topic imported proj:bk data <span style="color:#555555;">Ville <a href="http://www.bibsonomy.org/author/Tuulos">Tuulos</a> and Henry <a href="http://www.bibsonomy.org/author/Tirri">Tirri</a> </span>(<em>2004</em>)Mon Jul 23 20:24:37 CEST 2007Combining topic models and social networks for chat data mining 2004social mining network chat topic imported proj:bk data Site-based or topic-specific search engines work with mixed success because of the general difficulty of the information retrieval task, and the lack of good link information to allow authorities to be identifiedhttp://www.bibsonomy.org/bibtex/28415293f31343cb9644bf1f4dfffc8fa/wnpxrzwnpxrz2007-07-23T20:23:30+02:00proj:bk search engine opensource ir imported <span style="color:#555555;">Wray <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> and Jaakko <a href="http://www.bibsonomy.org/author/Lofström">Lofstr&#246;m</a> and Jukka <a href="http://www.bibsonomy.org/author/Perkiö">Perki&#246;</a> and Sami <a href="http://www.bibsonomy.org/author/Perttu">Perttu</a> and Vladimir <a href="http://www.bibsonomy.org/author/Poroshin">Poroshin</a> and Tomi <a href="http://www.bibsonomy.org/author/Silander">Silander</a> and Henry <a href="http://www.bibsonomy.org/author/Tirri">Tirri</a> and Antti <a href="http://www.bibsonomy.org/author/Tuominen">Tuominen</a> and Ville <a href="http://www.bibsonomy.org/author/Tuulos">Tuulos</a> </span>(<em>2004</em>)Mon Jul 23 20:23:30 CEST 2007Endjinn: a scalable topic-based open source search engine 2004proj:bk search engine opensource ir imported