BibSonomy publications for /user/diego_ma/folksonomyThu Jan 24 03:51:40 CET 2008Proc. FGIR 2006FolkRank: A Ranking Algorithm for Folksonomies2006folksonomy pagerank In social bookmark tools users are setting uplightweight conceptual structures called folksonomies. Currently,the information retrieval support is limited. We present a formalmodel and a new search algorithm for folksonomies, calledFolkRank, that exploits the structure of the folksonomy. Theproposed algorithm is also applied to find communities within thefolksonomy and is used to structure search results. All findings aredemonstrated on a large scale dataset. A long version of this paperhas been published at the European Semantic Web Conference2006.Thu Jan 24 02:12:25 CET 2008HeidelbergThe Semantic Web: Research and ApplicationsJune411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking40112006folksonomy Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. At themoment, however, the information retrieval support is limited. Wepresent a formal model and a new search algorithm for folksonomies,called \emph{FolkRank}, that exploits the structure of thefolksonomy. The proposed algorithm is also applied to findcommunities within the folksonomy and is used to structure searchresults. All findings are demonstrated on a large scale dataset.Thu Jan 24 02:12:23 CET 2008AI Communications Journal, Special Issue on "Network Analysis in Natural Sciences and Engineering"Network Properties of Folksonomies2007folksonomy network Wed Jan 09 07:02:53 CET 2008D-Lib Magazine1Folksonomies: Tidying up Tags?122006folksonomy imported Wed Jan 09 06:55:27 CET 2008Int’l Journal on Semantic Web & Information Systems2Ontology of Folksonomy: A Mash-up of Apples and Oranges32007folksonomy ontology Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by the terms used in data that they might generate, share, or consume. Folksonomies are an emergent phenomenon of the Social Web. They arise from data about how people associate terms with content that they generate, share, or consume. Recently the two ideas have been put into opposition, as if they were right and left poles of a political spectrum. This is a false dichotomy; they are more like apples and oranges. In fact, as the Semantic Web matures and the Social Web grows, there is increasing value in applying Semantic Web technologies to the data of the Social Web. This article is an attempt to clarify the distinct roles for ontologies and folksonomies, and previews some new work that applies the two ideas together - an ontology of folksonomy.Wed Jan 09 03:27:57 CET 2008Journal of Artificial Intelligence Research181-212Knowledge Derived from Wikipedia for Computing Semantic Relatedness302007folksonomy Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet on some datasets. We also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, we show that our method can be easily used for languages other than English by computing semantic relatedness for a German dataset.Fri Dec 14 08:14:31 CET 2007Journal of Information ScienceMay2198--208The Structure of Collaborative Tagging Systems322005folksonomy 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.Fri Dec 14 07:36:02 CET 2007AI CommunicationsNetwork Properties of Folksonomies2007folksonomy networks Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two ofthe systems. We consider their underlying data structures ---so-called folksonomies --- as tri-partite hypergraphs, and adapt classical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.Fri Dec 14 07:34:29 CET 2007BanffProc. WWW2007 Workshop "Tagging and Metadata for Social Information Organization"MayAn extended version appeared in AI Communications.Network Properties of Folksonomies2007folksonomy 8Social resource sharing systems like YouTube and del.icio.us haveacquired a large number of users within the last few years. Theyprovide rich resources for data analysis, information retrieval, andknowledge discovery applications. A first step towards this end is togain better insights into content and structure of these systems. Inthis paper, we will analyse the main network characteristics of two ofthe systems. We consider their underlying data structures ---so-called folksonomies --- as tri-partite hypergraphs, and adaptclassical network measures like characteristic path length andclustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence andinvestigate some of its statistical properties, focusing oncorrelations in node connectivity and pointing out features thatreflect emergent semantics within the folksonomy. We show that simplestatistical indicators unambiguously spot non-social behavior such as spam.Fri Dec 14 02:43:00 CET 2007New York, NY, USAHYPERTEXT '06: Proceedings of the seventeenth conference on Hypertext and hypermedia31--40HT06, tagging paper, taxonomy, Flickr, academic article, to read2006web ontology folksonomy In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., "tags") to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems.Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photo-sharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.