BibSonomy publications for /tag/07Thu Oct 28 05:13:45 CEST 2010New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web845--854P-TAG: large scale automatic generation of personalized annotation tags for the web200707 Chirita content desktop recommendation tag The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.Wed Aug 04 09:43:28 CEST 2010Knowledge Discovery in Databases: PKDD 2007506--514Tag Recommendations in Folksonomies200707 Jaschke folkrank recommendation tag Collaborative tagging systems allow users to assign keywords{\^a}€”so called {\^a}€œtags{\^a}€{\^a}€”to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually includetag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabularyacross users. In practice, however, only very basic recommendation strategies are applied.Wed Apr 21 01:27:25 CEST 2010Knowledge Discovery in Databases: PKDD 2007506--514Tag Recommendations in Folksonomies200707 Jaschke folkrank recommendation tag Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually includetag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabularyacross users. In practice, however, only very basic recommendation strategies are applied.Fri Mar 05 19:50:57 CET 2010Information Processing & ManagementText Summarization61705 - 1714Using lexical chains for keyword extraction43200707 Ercan WordNet chains extraction keyword lexical text Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.Tue Feb 16 03:40:55 CET 2010San Francisco, CA, USAIJCAI'07: Proceedings of the 20th international joint conference on Artifical intelligence1606--1611Computing semantic relatedness using Wikipedia-based explicit semantic analysis200707 Gabrilovich relatedness semantics wikipedia Tue Feb 16 02:50:06 CET 2010New York, NY, USASIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval787--788Clustering short texts using wikipedia200707 Banerjee clustering semantic short texts wikipedia Sat Jan 30 05:29:47 CET 2010Proceedings of the National Academy of Sciences (PNAS)January51461--1464Semiotic dynamics and collaborative tagging104200707 Cattuto dynamics folksonomy modelling semiotic 3010.1073/pnas.0610487104 Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag cooccurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: () a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; () a notion of memory, or aging of resources, in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features with a surprisingly high accuracy. This points in the direction of a universal behavior of users who, despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity, appear to follow simple activity patterns.Tue Jan 26 03:41:30 CET 2010Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference onDec.393-398Comparison of semantic and single term similarity measures for clustering turkish documents200707 Yucesoy clustering semantic similarity term turkish With the rapid growth of the World Wide Web (www), it becomes a critical issue to design and organize the vast amounts of on-line documents on the web according to their topic. Even for the search engines it is very important to group similar documents in order to improve their performance when a query is submitted to the system. Clusterng is useful for taxonomy design and similarity search of documents on such a domain. Similarity is fundamental to many clustering applications on hypertext. In this paper, we will study how measures of similarity are used to cluster a collection of documents on a web site. Most of the document clustering techniques rely on single term analysis of text, such as vector space model. To better group of related documents we propose a new semantic similarity measure. We compare our measure with Wu-Palmer similarity and cosine similarity. Experimental results show that cosine similarity perform better than the semantic similarities. We demonstrate our results on Turkish documents. This is a first study that considers the semantic similarities between Turkish documents.Tue Jan 26 01:34:43 CET 2010New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web757--766Measuring semantic similarity between words using web search engines200707 Bollegala engine search semantic similarity term Fri Jan 08 03:21:33 CET 2010New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web835--844Analysis of topological characteristics of huge online social networking services200707 Ahn networks sampling snowball social Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.Thu Jan 07 01:37:21 CET 2010Amsterdam, The Netherlands, The NetherlandsWeb Semant.15--15Ontologies are us: A unified model of social networks and semantics5200707 Mika folksonomy model ontology 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.Mon Jan 04 02:15:06 CET 2010New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web211--220The complex dynamics of collaborative tagging200707 Halpin collaborative complex dynamics law model power tagging The debate within the Web community over the optimal means by which to organize information often pits formalized classifications 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 delicio. 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.Sun Jan 03 22:17:10 CET 2010School of Information, University of Michigan; School of Information, University of MichiganProceedings of the American Society for Information Science and Technology11-13Public bookmarks and private benefits: An analysis of incentives in social computing44200707 Wash folksonomy model personal Sun Sep 13 21:50:17 CEST 2009Special Issue of the Computer Networks journal on Innovations in Web Communications Infrastructure164574--4585Suppporting Collaborative Hierarchical Classification: Bookmarks as an Example51200707 @sugesstionMalnisFall09 Benz classification folksonomy hierarchical Bookmarks (or favorites, hotlists) are popular strategies to relocate interesting websites on the WWW by creating a personalized URL repository. Most current browsers offer a facility to locally store and manage bookmarks in a hierarchy of folders; though, with growing size, users reportedly have trouble to create and maintain a stable organization structure. This paper presents a novel collaborative approach to ease bookmark management, especially the “classification” of new bookmarks into a folder. We propose a methodology to realize the collaborative classification idea of considering how similar users have classified a bookmark. A combination of nearest-neighbor-classifiers is used to derive a recommendation from similar users on where to store a new bookmark. A prototype system called CariBo has been implemented as a plugin for the central bookmark server software SiteBar. All findings have been evaluated on a reasonably large scale, real user dataset with promising results, and possible implications for shared and social bookmarking systems are discussed.Tue Apr 21 21:56:25 CEST 2009Berlin, HeidelbergESWC '07: Proceedings of the 4th European conference on The Semantic Web503--517What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content200707 @sugesstionMalnisWinter10 Auer extraction semantics wiki Tue Mar 10 03:51:19 CET 2009New York, NY, USAACM Trans. Database Syst.430NaLIX: A generic natural language search environment for XML data32200707 Li Nalix XML language natural queries search Tue Mar 10 03:47:00 CET 2009New York, NY, USACIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management505--514An experimental study of the impact of information extraction accuracy on semantic search performance200707 @sugesstionMalnisWinter10 Chu extraction information noise search semantics Mon Mar 09 16:20:22 CET 2009Proceedings of the Workshop on Tagging and Metadata for Social Information Organization, 16th International World Wide Web ConferenceLearning user profiles from tagging data and leveraging them for personal(ized) information access200707 Michlmayr extraction profiles social tagging toread user Sun Jan 11 23:02:32 CET 2009Bioinformatics Research and Applications49--60A Multi-Stack Based Phylogenetic Tree Building Method200707 Busa-Fekete UPGMA agglomerative clustering tree Here we introduce a new Multi-Stack (MS) based phylogenetic tree building method. The Multi-Stack approach organizes the candidate subtrees (i.e. those having same number of leaves) into limited priority queues, always selecting the K-best subtrees, according to their distance estimation error. Using the K-best subtrees our method iteratively applies a novel subtree joining strategy to generate candidate higher level subtreesfrom the existing low-level ones. This new MS method uses the Constrained Least Squares Criteria (CLSC) which guarantees thenon-negativity of the edge weights.Mon Dec 15 20:40:32 CET 2008Evolutionary Computation, IEEE Transactions onFeb. 156-76An Evolutionary Approach to Multiobjective Clustering11200707 Handl algorithm clustering genetic multiobjective The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits07Community for tag(s) 07