http://www.bibsonomy.org/burst/user/wnpxrz/proj:etBibSonomy BuRST Feed for /user/wnpxrz/proj:et2008-10-08T13:12:25+02:00http://www.bibsonomy.org/bibtex/2fcdd690b58d350cb61b107bbd18b8221/wnpxrzwnpxrz2008-01-12T11:43:50+01:00proj:et toread ontology proj:o4p av:attached <span style="color:#555555;">Nicola <a href="http://www.bibsonomy.org/author/Guarino">Guarino</a> und P. <a href="http://www.bibsonomy.org/author/Giaretta">Giaretta</a> </span><em>Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, </em><em>Seite25-32. </em><em>#IOS#, </em>(<em>1995</em>)Sat Jan 12 11:43:50 CET 2008Towards {V}ery {L}arge {K}nowledge {B}ases: {K}nowledge {B}uilding and {K}nowledge {S}haring25-32Ontologies and {K}nowledge {B}ases: {T}owards a {T}erminological {C}larification1995proj:et toread ontology proj:o4p av:attached The word "ontology" has recently gained a good popularity within the knowledge engineering community. {H}owever, its meaning tends to remain a bit vagua, as the term is used in very different ways. {L}imiting our attention to the various proposals made in the current debate in {AI}, we isolate a number of interpretations, which in our opinion deserve a suitable clarification. {W}e elucidatethe implications of such various interpretations, arguing for the need of clear terminological choices regarding the technical use of terms like "ontology", "conceptualization" and "ontological commitment". {A}fter some comments on the use "{O}ntology" (with the capital "o") as a term which denotes a philosophical discipline, we analyse the possible confusion between an ontology intended as a particular conceptual framework at the knowledge level and an ontology intended as a concrete artifact at the symbol level, to be used for a given purpose. {A} crucial point in this clarification e�ort is the careful analysis of {G}ruber's definition of an ontology as a specification of a conceptualization.CiteULike: Improving Tag-Clouds as Visual Information Retrieval Interfaceshttp://www.bibsonomy.org/bibtex/206f68f9fe46dc6d0f646d932e428dec9/wnpxrzwnpxrz2007-12-03T17:37:36+01:00tag visualization proj:et proj:bk cloid ir tagging <span style="color:#555555;">Y. <a href="http://www.bibsonomy.org/author/Hassan-Montero">Hassan-Montero</a> und 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 Interfaces2006tag visualization proj:et proj:bk cloid ir tagging 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.Just-in-time information retrieval agentshttp://www.bibsonomy.org/bibtex/2e6b063285a51f88aa566d08a0a693416/wnpxrzwnpxrz2007-11-25T20:18:34+01:00proj:et imported ir agent <span style="color:#555555;">B. J. <a href="http://www.bibsonomy.org/author/Rhodes">Rhodes</a> und P. <a href="http://www.bibsonomy.org/author/Maes">Maes</a> </span><em>IBM Syst. J.</em><em>39(3-4):685--704</em>(<em>2000</em>)Sun Nov 25 20:18:34 CET 2007Riverton, NJ, USAIBM Syst. J.3-4685--704Just-in-time information retrieval agents392000proj:et imported ir agent A just-in-time information retrieval agent (JITIR agent) is software that proactively retrieves and presents information based on a person's local context in an easily accessible yet nonintrusive manner. This paper describes three implemented JITIR agents: the Remembrance Agent, Margin Notes, and Jimminy. Theory and design lessons learned from these implementations are presented, drawing from behavioral psychology, information retrieval, and interface design. They are followed by evaluations and experimental results. The key lesson is that users of JITIR agents are not merely more efficient at retrieving information, but actually retrieve and use more information than they would with traditional search engines.Using Physical Context for Just-in-Time Information Retrievalhttp://www.bibsonomy.org/bibtex/2bec14e95a1815cbe05c19912d0ab03c6/wnpxrzwnpxrz2007-11-25T20:04:22+01:00imported context physical proj:et ir <span style="color:#555555;">Bradley <a href="http://www.bibsonomy.org/author/Rhodes">Rhodes</a> </span><em>IEEE Trans. Comput.</em><em>52(8):1011--1014</em>(<em>2003</em>)Sun Nov 25 20:04:22 CET 2007Washington, DC, USAIEEE Trans. Comput.81011--1014Using Physical Context for Just-in-Time Information Retrieval522003imported context physical proj:et ir Abstract¿Jimminy is a wearable personal note-taking and note-archival application that automatically displays notes that might be relevant to the wearer in his current environment. The system selects old notes to show on a head-up display based on the wearer's current location, people in the immediate area, and the subject-line and contents of any current notes being written. This paper describes an experiment that evaluates the usefulness of the wearer's physical context (location and people in the area) for automatically finding useful archived information. The results suggest that, while physical context can be used to discover useful archived notes, the subject and text of notes currently being entered are a much better indicator of usefulness in the personal note-taking domain.Ontologies are ushttp://www.bibsonomy.org/bibtex/2db2140398fe061b8f99cad45cf438e3e/wnpxrzwnpxrz2007-08-30T18:44:00+02:00network ontology imported social proj:et <span style="color:#555555;">Peter <a href="http://www.bibsonomy.org/author/Mika">Mika</a> </span><em>Web Semant.</em><em>5(1):5--15</em>(<em>2007</em>)Thu Aug 30 18:44:00 CEST 2007Amsterdam, The Netherlands, The NetherlandsWeb Semant.15--15Ontologies are us: A unified model of social networks and semantics52007network ontology imported social proj:et In our work the traditional bipartite model of ontologies is extended with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.IngentaConnect Social networking on the semantic webhttp://www.bibsonomy.org/bibtex/256accd549693c5618f7d2b0cd216b8f2/wnpxrzwnpxrz2007-08-30T18:43:15+02:00imported network semanticweb social proj:et <span style="color:#555555;">Tim <a href="http://www.bibsonomy.org/author/Finin">Finin</a> und Li <a href="http://www.bibsonomy.org/author/Ding">Ding</a> und Lina <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> und Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> </span><em>The Learning Organization: An International Journal</em>(<em>1 May 2005</em>)Thu Aug 30 18:43:15 CEST 2007The Learning Organization: An International Journal418-435(18)Social networking on the semantic web121 May 2005imported network semanticweb social proj:et Purpose - Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach - The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the &quot;Friend of a Friend&quot; (FOAF) ontology. The datasets were analyzed to discover how FOAF is being used and investigate the kinds of social networks found on the web. Findings - The FOAF ontology is the most widely used domain ontology on the semantic web. People are using it in an open and extensible manner by defining new classes and properties to use with FOAF. Research limitations/implications - RDF data was only obtained from public RDF documents published on the web. Some RDF FOAF data may be unavailable because it is behind firewalls, on intranets or stored in private databases. The ways in which the semantic web languages RDF and OWL are being used (and abused) are dynamic and still evolving. A similar study done two years from now may show very different results. Originality/value - This paper describes how social networks are being encoded and used on the world wide web in the form of RDF documents and the FOAF ontology. It provides data on large social networks as well as insights on how the semantic web is being used in 2005.WSDhttp://www.bibsonomy.org/bibtex/289f2b37624703f3e2979ff35f53ebff2/wnpxrzwnpxrz2007-08-30T18:34:25+02:00user modeling proj:et <span style="color:#555555;">I. <a href="http://www.bibsonomy.org/author/Zuckerman">Zuckerman</a> und D. <a href="http://www.bibsonomy.org/author/Albrecht">Albrecht</a> </span><em>User Modeling and User Adaptive Interaction</em><em>11(1-2):5-18</em>(<em>2001</em>)Thu Aug 30 18:34:25 CEST 2007User Modeling and User Adaptive Interaction1-25-18Predictive statistical Models for User Modeling112001user modeling proj:et Interactive Cluster Visualization for Information Retrieval - Allan, Leouski, Swan (ResearchIndex)http://www.bibsonomy.org/bibtex/21e812543ac2929cf85e5ee475efe3c36/wnpxrzwnpxrz2007-08-30T18:26:20+02:00visualization ir proj:et search imported clustering <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Allan">Allan</a> und A. <a href="http://www.bibsonomy.org/author/Leouski">Leouski</a> und R. <a href="http://www.bibsonomy.org/author/Swan">Swan</a> </span>(<em>1997</em>)Thu Aug 30 18:26:20 CEST 2007Interactive Cluster Visualization for Information Retrieval1997visualization ir proj:et search imported clustering This study investigates the ability of cluster visualization to help a user rapidly identify relevant documents. It provides added support for the truth of the Cluster Hypothesis on retrieved documents and shows that clustering of relevant documents is readily visible. The study then shows the visual effect of a technique similar to relevance feedback and shows how to enhance that effect to further help the user locate relevant material. A ranked list returned by a text search engine purports...SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2ca77efe737316579fdbdfb49f72e9cd4/wnpxrzwnpxrz2007-08-30T18:09:43+02:00imported tagging semanticweb folksonomy proj:et <span style="color:#555555;">Lucia <a href="http://www.bibsonomy.org/author/Specia">Specia</a> und Enrico <a href="http://www.bibsonomy.org/author/Motta">Motta</a> </span><em>The Semantic Web: Research and Applications</em>(<em>2007</em>)Thu Aug 30 18:09:43 CEST 2007The Semantic Web: Research and Applications624--639Integrating Folksonomies with the Semantic Web2007imported tagging semanticweb folksonomy proj:et While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approachfor making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organizationcan emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processingstrategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminaryresults on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highlyrelated tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.ePrints.FRI - Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocshttp://www.bibsonomy.org/bibtex/2b4564953de8c71b10e0c8f163379a216/wnpxrzwnpxrz2007-07-25T21:04:13+02:00proj:et clustering proj:gtf imported data analysis government <span style="color:#555555;">Aleks <a href="http://www.bibsonomy.org/author/Jakulin">Jakulin</a> und Wray <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> </span>(<em>2004</em>)Wed Jul 25 21:04:13 CEST 2007Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs2004proj:et clustering proj:gtf imported data analysis government bibliografica tesihttp://www.bibsonomy.org/bibtex/265c6f348a54f872fb3e60b4bd64b485b/wnpxrzwnpxrz2007-06-28T13:50:01+02:00proj:tags network collaborative tagging proj:et <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Lambiotte">Lambiotte</a> und M. <a href="http://www.bibsonomy.org/author/Ausloos">Ausloos</a> </span><em>Dec2005. </em>Thu Jun 28 13:50:01 CEST 2007DecCollaborative tagging as a tripartite network2005proj:tags network collaborative tagging proj:et We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... <br />To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.http://www.bibsonomy.org/bibtex/2631924a8b2f1ab8a8e2c38a43f1dbc5f/wnpxrzwnpxrz2007-06-28T13:49:10+02:00proj:et dynamic proj:tags tagging collaborative <span style="color:#555555;">Harry <a href="http://www.bibsonomy.org/author/Halpin">Halpin</a> und Valentin <a href="http://www.bibsonomy.org/author/Robu">Robu</a> und Hana <a href="http://www.bibsonomy.org/author/Shepherd">Shepherd</a> </span><em>Proceedings of the 16th nternational World Wide Web Conference (WWW'07), </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2007</em>)Thu Jun 28 13:49:10 CEST 2007New York, NY, USAProceedings of the 16th nternational World Wide Web Conference (WWW'07)The Complex Dynamics of Collaborative Tagging2007proj:et dynamic proj:tags tagging collaborative The debate within the Web community over the optimal means by which to organize information often pits formalized classications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for “popular” sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.Improved annotation of the blogosphere via autotagging and hierarchical clusteringhttp://www.bibsonomy.org/bibtex/25c9c83e89da2faa8906a5927fe7ca3ef/wnpxrzwnpxrz2007-06-28T13:48:40+02:00blogs tagging proj:tags clustering blogosphere proj:et proj:bk <span style="color:#555555;">Christopher H. <a href="http://www.bibsonomy.org/author/Brooks">Brooks</a> und Nancy <a href="http://www.bibsonomy.org/author/Montanez">Montanez</a> </span><em>WWW '06: Proceedings of the 15th international conference on World Wide Web, </em><em>Seite625-632. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2006</em>)Thu Jun 28 13:48:40 CEST 2007New York, NY, USAWWW '06: Proceedings of the 15th international conference on World Wide Web625-632Improved annotation of the blogosphere via autotagging and hierarchical clustering2006blogs tagging proj:tags clustering blogosphere proj:et proj:bk http://www.bibsonomy.org/bibtex/2b652b0d73b964ebc282c3c3060c01c9d/wnpxrzwnpxrz2007-06-28T13:46:10+02:00search tagging web proj:tags collaborative proj:et <span style="color:#555555;">Peng <a href="http://www.bibsonomy.org/author/Han">Han</a> und Zhimei <a href="http://www.bibsonomy.org/author/Wang">Wang</a> und Zhiyun <a href="http://www.bibsonomy.org/author/Li">Li</a> und Bernd <a href="http://www.bibsonomy.org/author/Kramer">Kramer</a> und Fan <a href="http://www.bibsonomy.org/author/Yang">Yang</a> </span><em>Web Intelligence, </em><em>Seite757-760. </em><em>IEEE Computer Society, </em>(<em>2006</em>)Thu Jun 28 13:46:10 CEST 2007Web Intelligenceconf/webi/2006757-760Substitution or Complement: An Empirical Analysis on the Impact of Collaborative Tagging on Web Search.2006search tagging web proj:tags collaborative proj:et http://www.bibsonomy.org/bibtex/2f456bf104893b7ebf6d373789ad7f561/wnpxrzwnpxrz2007-06-28T13:44:37+02:00network dynamic proj:et <span style="color:#555555;">Shawndra <a href="http://www.bibsonomy.org/author/Hill">Hill</a> und Deepak K. <a href="http://www.bibsonomy.org/author/Agarwal">Agarwal</a> und Robert <a href="http://www.bibsonomy.org/author/Bell">Bell</a> und Chris <a href="http://www.bibsonomy.org/author/Volinsky">Volinsky</a> </span><em>Journal of Computational &amp;#38; Graphical Statistics</em>(<em>September 2006</em>)Thu Jun 28 13:44:37 CEST 2007Journal of Computational &#38; Graphical Statistics584-608(25)Building an Effective Representation for Dynamic Networks15September 2006network dynamic proj:et A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks. http://www.bibsonomy.org/bibtex/2068214d888a030f4901ad471ed7c7bec/wnpxrzwnpxrz2007-06-28T13:41:56+02:00proj:bk proj:et text clustering <span style="color:#555555;">F. <a href="http://www.bibsonomy.org/author/Beil">Beil</a> und M. <a href="http://www.bibsonomy.org/author/Ester">Ester</a> und X. <a href="http://www.bibsonomy.org/author/Xu">Xu</a> </span><em>Proceedings of the International Conference on KnowledgeDiscovery and Data Mining, </em>(<em>2002</em>)Thu Jun 28 13:41:56 CEST 2007Proceedings of the International Conference on KnowledgeDiscovery and Data MiningFrequent Term-Based Text Clustering2002proj:bk proj:et text clustering PASCAL -http://www.bibsonomy.org/bibtex/295a6dda0f790963075654b67d0ab0cdb/wnpxrzwnpxrz2007-06-28T13:19:09+02:00proj:s2s semantic search ir imported proj:et proj:bk toread <span style="color:#555555;">Wray <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> und Kimmo <a href="http://www.bibsonomy.org/author/Valtonen">Valtonen</a> und Michael P. <a href="http://www.bibsonomy.org/author/Taylor">Taylor</a> </span>(<em>2005</em>)Thu Jun 28 13:19:09 CEST 2007The ALVIS Document Model for a Semantic Search Engine2005proj:s2s semantic search ir imported proj:et proj:bk toread Optimizing and profiling users online with Bayesian probabilistic modeling (ResearchIndex)http://www.bibsonomy.org/bibtex/22e952850432f9df8eed72f7ccfcfe7ea/wnpxrzwnpxrz2007-06-28T13:10:56+02:00imported probabilistic paper toread proj:bk proj:et profiling bayesian <span style="color:#555555;">Petri <a href="http://www.bibsonomy.org/author/Nokelainen">Nokelainen</a> und Henry <a href="http://www.bibsonomy.org/author/Tirri">Tirri</a> und Miikka <a href="http://www.bibsonomy.org/author/Miettinen">Miettinen</a> und Tomi <a href="http://www.bibsonomy.org/author/Silander">Silander</a> </span>(<em>2002</em>)Thu Jun 28 13:10:56 CEST 2007Optimizing and profiling users online with Bayesian probabilistic modeling2002imported probabilistic paper toread proj:bk proj:et profiling bayesian A Minimum Encoding Approach to Document Clustering (ResearchIndex)http://www.bibsonomy.org/bibtex/21a26d23ed4b73b13a97ed2f674128cf9/wnpxrzwnpxrz2007-06-28T13:09:05+02:00clustering proj:et paper mml imported proj:bk mdl document toread <span style="color:#555555;">Kimmo <a href="http://www.bibsonomy.org/author/Palin">Palin</a> und Janne <a href="http://www.bibsonomy.org/author/Pasanen">Pasanen</a> und Teemu <a href="http://www.bibsonomy.org/author/Roos">Roos</a> </span>(<em>2002</em>)Thu Jun 28 13:09:05 CEST 2007A Minimum Encoding Approach to Document Clustering2002clustering proj:et paper mml imported proj:bk mdl document toread Bayesian Analysis of Online Newspaper Log Data - Wettig, Lahtinen, Lepola, Myllymaki, Tirri (ResearchIndex)http://www.bibsonomy.org/bibtex/214ed408c8c37b150df5dfb20f42755f4/wnpxrzwnpxrz2007-06-28T13:05:55+02:00imported bayesian paper log proj:et toread <span style="color:#555555;">H. <a href="http://www.bibsonomy.org/author/Wettig">Wettig</a> und J. <a href="http://www.bibsonomy.org/author/Lahtinen">Lahtinen</a> und T. <a href="http://www.bibsonomy.org/author/Lepola">Lepola</a> und P. <a href="http://www.bibsonomy.org/author/Myllymaki">Myllymaki</a> und H. <a href="http://www.bibsonomy.org/author/Tirri">Tirri</a> </span>(<em>2003</em>)Thu Jun 28 13:05:55 CEST 2007Bayesian analysis of online newspaper log data2003imported bayesian paper log proj:et toread