Group items tagged

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

Open Context features an innovative folksonomy system that will encourage individual users to add value to the information in Open Context. This powerful social software allows users to add meaningful tags (keywords) to data they discover in their searches.

There is a revolution happening on the Internet that is alive and building momentum with each passing tag. With the advent of social software and Web 2.0, we usher in a new era of Internet order. One in which the user has the power to effect their own online experience, and contribute to others'. Today, users are adding metadata and using tags to organize their own digital collections, categorize the content of others and build bottom-up classification systems. The wisdom of crowds, the hive mind, and the collective intelligence are doing what heretofore only expert catalogers, information architects and website authors have done. They are categorizing and organizing the Internet and determining the user experience, and it's working. No longer do the experts have the monopoly on this domain; in this new age users have been empowered to determine their own cataloging needs. Metadata is now in the realm of the Everyman.

Using segments of rich media makes it possible to aggregate context and meaning on these chunks by using a number of different mechanisms. Starting with a granular node -- be it a sound bite, visual clip or written fact -- it is possible to aggregate contextual metadata through a series of steps that emergently progress from: * Starting with thousands of defined Audio Sound Bites & visual clips * Rating sound bites and clustering them with folksonomy tags * Sequencing audio sound bites within playlists * Collaboratively building larger sequences with nested playlists * Independently controlling the video & audio tracks with 2-dimensional nested playlists * Evaluating Multiple Storylines and Hypotheses with a 2-dimensional playlist matrix * Visualizing complex networks by mapping out feedback loop relationships between nodes

Faviki is a social bookmarking tool that allows users to annotate the contents of web pages by Wikipedia concepts. Using Wikipedia as a source of a universal controlled vocabulary, it provides so-called 'semantic tags' which are standardized and computer-interpretable. In this way, Faviki is able to solve some common problems related to classic 'folksonomy' tags, in particular: polysemy, synonymy, different lexical forms, and lack of a commonly agreed meaning of terms. In a wider perspective, Faviki aims to speed up the transition from Web 2.0 to the Semantic Web.

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.

On January 07, 2009, OASIS announced the submission of a draft charter for a new OASIS Technical Committee to define an integrated collaboration object model supporting a complete range of enterprise collaboration activities. The proposed data model is based upon the Oracle Beehive Object Model (BOM), to be contributed by Oracle to the ICOM TC. The new standard model, interface, and protocol would support contextual collaboration within business processes for an integrated collaboration environment which includes communication artifacts (e.g., email, instant message, telephony, RSS), teamwork artifacts (such as project and meeting workspaces, discussion forums, real-time conferences, presence, activities, subscriptions, wikis, and blogs), content artifacts (e.g., text and multi-media contents, contextual connections, taxonomies, folksonomies, tags, recommendations, social bookmarking, saved searches), and coordination artifacts (such as address books, calendars, tasks) etc.

Keotag lets users search for tags across 14 different sites, from Reddit to Ice Rocket.Keotag will also generate folksonomy tags for a blog post, or submit a bookmark to multiple sites.

Your Home for Faceted Classification Tools Facetmap is both a data model and a software package, created to let users browse complex metadata while retaining a simple, familiar, menu interface.

This is the official website of the TAGora project, a STREP project funded by the European Commission in the framework of the FET proactive initiative "Simulating Emergent Properties in Complex Systems".

Functional Requirements for Sharing Tag Data The TagCommons effort is operating something like a software project. The process is very lightweight, but has an important step that is often forgotten in discussions about ontologies and formats: for what purposes are we designing this? These days, a good way to look at functional requirements for software is to identify use cases and then derive engineering requirements. This was the first outcome of the working group, and the results are summarized here. We will describe the use cases first and then the requirements.

Proceedings of the 2nd SIRTEL'08 Workshop on Social Information Retrieval for Technology Enhanced Learning Maastricht, Netherlands, September 17, 2008

Tagging refers to the process by which users assign terms meaningful to them to a resource in the online environment. The rise of social bookmarking Web sites have skyrocketed tagging systems into the mainstream.

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.

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