BibSonomy publications for /user/berrueta/ontologyMon Dec 17 14:59:49 CET 2007OTM Workshops (2)conf/otm/2007-w21222-1232Lecture Notes in Computer ScienceOntoGame: Towards Overcoming the Incentive Bottleneck in Ontology Building.480620072007 collaborative esp game ontogame ontology tagging wikipedia Fri Dec 14 17:46:12 CET 2007{WonderWeb} Deliverable {D17}. The {WonderWeb} Library of Foundational Ontologies and the {DOLCE} ontology2002dolce foundational ontology turruta-eswc2008 wonderweb Fri Dec 14 09:37:38 CET 2007Handbook on Ontologies151-159An Overview of OntoClean2004ontoclean ontology turruta-eswc2008 Fri Dec 14 09:36:35 CET 2007Proceedings of the 2001 IJCAI Workshop on Ontologies and Information SharingUnderstanding top-level ontological distinctions2001dolce ontology turruta-eswc2008 Wed 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.Tue Nov 13 16:37:13 CET 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandMayInducing Ontology from Flickr Tags2006ezweb flickr folksonomy ontology tagging 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 16:12:31 CET 2007HeidelbergThe Semantic Web - ISWC 2005, Proceedings of the 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10522-536Lecture Notes in Computer ScienceOntologies Are Us: A Unified Model of Social Networks and Semantics.372920052005 folksonomy ontology In our work we extend the traditional bipartite model of ontologies 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 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 17:31:55 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.Sat Oct 27 13:57:04 CEST 2007The Semantic Web: Real-World Applications from Industryconf/swb/2007rwai243-272Semantic Web And Beyond Computing for Human ExperienceOntology-Based Knowledge Management In The Steel Industry.720072007 me ontology springer steel