BibSonomy publications for /user/berrueta/ezwebWed Nov 14 15:38:25 CET 2007Proceedings of the First Workshop on Semantic Wikis -- From Wiki to Semantics, co-located with the 3rd Annual European Semantic Web Conference (ESWC 2006)SemWiki2006-proceedingsJuneWorkshop on Semantic WikisHarvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements20062006 ezweb folksonomy ontology wikipedia One major obstacle towards adding machine-readable annotation to existing Web content is the lack of domain ontologies. While FOAF and Dublin Core are popular means for expressing relationships between Web resources and between Web resources and literal values, we widely lack unique identifiers for common concepts and instances. Also, most available ontologies have a very weak community grounding in the sense that they are designed by single individuals or small groups of individuals, while the majority of potential users is not involved in the process of proposing new ontology elements or achieving consensus. This is in sharp contrast to natural language where the evolution of the vocabulary is under the control of the user community. At the same time, we can observe that, within Wiki communities, especially Wikipedia, a large number of users is able to create comprehensive domain representations in the sense of unique, machine-feasible, identifiers and concept definitions which are sufficient for humans to grasp the intension of the concepts. The English version of Wikipedia contains now more than one million entries and thus the same amount of URIs plus a human-readable description. While this collection is on the lower end of ontology expressiveness, it is likely the largest living ontology that is available today. In this paper, we (1) show that standard Wiki technology can be easily used as an ontology development environment for named classes, reducing entry barriers for the participation of users in the creation and maintenance of lightweight ontologies, (2) prove that the URIs of Wikipedia entries are surprisingly reliable identifiers for ontology concepts, and (3) demonstrate the applicability of our approach in a use case.Wed Nov 14 15:06:56 CET 2007Galeway, IrelandProceedings of the Workshop on End User Semantic Web Interaction, held in conjunction with the International Semantic Web Conference 2005 (ISWC'05)NovemberA Proposal of Community-based Folksonomy with RDF Metadata2005ezweb foaf folksonomy rdf Tue Nov 13 16:37:13 CET 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandMayInducing Ontology from Flickr Tags2006ezweb flickr folksonomy ontology tagging Tue Nov 13 10:24:59 CET 2007Journal of Information Science2198-208The Structure of Collaborative Tagging Systems3220062005 collaborative delicious ezweb folksonomy tagging Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.Mon Nov 12 18:00:35 CET 2007Banff, CanadaProc. WWW 2007 Workshop on Tagging and Metadata for Social Information OrganizationMaySemKey: A Semantic Collaborative Tagging System 20072007 ezweb folksonomy ontology semantic semkey www Mon Nov 12 17:47:14 CET 2007Lecture Notes in Computer Science39931114 - 1117Collaborative tagging as a tripartite network20062006 collaborative ezweb folksonomy tagging 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.Mon Nov 12 13:26:50 CET 20074th European Semantic Web ConferenceIntegrating Folksonomies with the Semantic Web20072007 eswc ezweb folksonomy semanticweb Abstract. 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 approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified. Mon Nov 12 10:46:39 CET 2007Proceedings of International Conference on Weblogs and Social Media{Using Ontologies to Strengthen Folksonomies and Enrich Information Retrieval in Weblogs}20072007 ezweb folksonomy ir ontology sioc Sat Nov 10 17:37:19 CET 2007GI Jahrestagung (2)conf/gi/2006-2305-312LNIEmergent Semantics in BibSonomy.9420062006 bibsonomy ezweb folksonomy semantics Sat Nov 10 17:35:37 CET 2007AI Communications Journal, Special Issue on "Network Analysis in Natural Sciences and Engineering" (accepted for publication)Network Properties of Folksonomies20072007 ezweb folksonomy Sat Nov 10 17:31:55 CET 2007HeidelbergData Science and Classification. Proceedings of the 10th IFCS Conf.July261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 ezweb folksonomy ontology Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.Sat Nov 10 17:18:46 CET 2007Bridging the Gep between Semantic Web and Web 2.0 (SemNet 2007)57-70FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies20072007 ezweb folksonomy ontology We can observe that the amount of non-toy domain ontologies is still very limited for many areas of interest. In contrast, folksonomies are widely in use for (1) tagging Web pages (e.g. del.icio.us), (2) annotating pictures (e.g. flickr), or (3) classifying scholarly publications (e.g. bibsonomy). However, such folksonomies cannot offer the expressivity of ontologies, and the respective tags often lack a context-independent and intersubjective definition of meaning. Also, folksonomies and other unsupervised vocabularies frequently suffer from inconsistencies and redundancies. In this paper, we argue that the social interaction manifested in folksonomies and in their usage should be exploited for building and maintaining ontologies. Then, we sketch a comprehensive approach for deriving ontologies from folksonomies by integrating multiple resources and techniques. In detail, we suggest combining (1) the statistical analysis of folksonomies, associated usage data, and their implicit social networks, (2) online lexical resources like dictionaries, Wordnet, Google and Wikipedia, (3) ontologies and Semantic Web resources, (4) ontology mapping and matching approaches, and (5) functionality that helps human actors in achieving and maintaining consensus over ontology element suggestions resulting from the preceding steps.