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"The debate within the Web community over the optimal means by which to organize information often pits formalized classications 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 del.icio.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."

The paper shows that the tags users choose are not chaotic, but rather quickly converge to a common descriptive set of tags that is almost unchanging over time. Perhaps once the tags have stabilized, coherent URI-based identification schemes could emerge?

Nice paper, thanks. Categories / tags / subjects / topics / issues ... that's what I'm working with right now. p.s. sure would be nice if the email notification included the source URL. I'm far more likely to download the PDF when I see something like www2007.org/paper635.pdf

tagCare lets you to maintain all your tags jointly in one place, which is especially useful if you normally use several of these different platforms. Many users apply a variety of different tags within different platforms - and finally get lost among them. For example, it is hard to keep track of consistent spelling variants (e.g. not using "science_fiction" in one case and "scienceFiction" in the other) or of preferred terms (e.g. not using "bike" in one case and "bicycle" in the other). Some documents may be tagged with the general term "dog", others more specifically with "greyhound" or "border_collie". tagCare will help you to apply some structure to your tagging vocabulary so that you will more easily navigate through vocabulary choices and use tags more consistently. In tagCare, a user can assemble all tags which he has used within different systems and may then create his own vocabulary hierarchy, synonym collections and cross-references to related terms to establish some lightweight form of controlled vocabulary. This process is also called "tag gardening". Edited and structured tags will then be used to browse document collections in other platforms and to directly tag documents out of tagCare. tagCare is still under development, a first demo version will be available soon and more features will then be added step by step. tagCare will first support Flickr, Bibsonomy and del.icio.us.

MOAT (Meaning Of A Tag) provides a Semantic Web framework to publish semantically-annotated content from free-tagging. While tags are widely used in Web 2.0 services, their lack of machine-understandable meaning can be a problem for information retrieval, especially when people use tags that can have different meanings depending on the context. MOAT aims to solve this by providing a way for users to define meaning(s) of their tag(s) using URIs of Semantic Web resources (such as URIs from dbpedia, geonames … or any knowledge base), and then annotate content with those URIs rather than free-text tags, leveraging content into Semantic Web, by linking data together. Moreover, tag meanings can be shared between people, providing an architecture of participation to define and exchange potential meanings of tags within a community of users. To achieve this goal, MOAT relies on an architecture that can be deployed for any organisation or community and that involves a lightweight ontology, a MOAT server, and some third-party clients .

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.

Extreme Tagging Systems allow for Tagging the tags and Tagging relations between the tags.

Common Tag is an open tagging format developed to make content more connected, discoverable and engaging. Unlike free-text tags, Common Tags are references to unique, well-defined concepts, complete with metadata and their own URLs. With Common Tag, site owners can more easily create topic hubs, cross-promote their content, and enrich their pages with free data, images and widgets.

Tagging is a process that associates keywords with specific content. We did a rough analysis in our paper (reference below), and computed how often a keyword used by a user to tag an URL appears in the page content. We found that, on average, the chance that a tag comes from the content is 49%. This process produced a conservative estimate of tag occurrence in content, since we did not account for situations such as content changes for a given URL (e.g., dynamic content), typos (e.g., "Ajaz" instead of "Ajax"), abbreviations (e.g., "ad" instead of "advertisement"), compound tags (e.g., "SearchEngine"), and tags written in languages other than that of the content.

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)

TagCrowd is a web application for visualizing word frequencies in any user-supplied text by creating what is popularly known as a tag cloud or text cloud. TagCrowd is taking tag clouds far beyond their original function: * as topic summaries for speeches and written works * as blog tool or website analysis for search engine optimization (SEO) * for visual analysis of survey data * as brand clouds that let companies see how they are perceived by the world * for data mining a text corpus * for helping writers and students reflect on their work * as name tags for conferences, cocktail parties or wherever new collaborations start * as resumes in a single glance * as visual poetry

Faviki is a social bookmarking tool which allows you to tag webpages you want to remember with Wikipedia terms. This means that everybody uses the same names for tags from the world's largest collection of knowledge. Thanks to DBpedia, which extracts structured information from Wikipedia and represents it in a flexible data model, these tags are reference to objects which are categorized automatically, keeping your and your friend's bookmarks and interests well organized.

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.

KML is a file format used to display geographic data in an Earth browser such as Google Earth, Google Maps, and Google Maps for mobile. KML uses a tag-based structure with nested elements and attributes and is based on the XML standard. All tags are case-sensitive and must be appear exactly as they are listed in the KML Reference. The Reference indicates which tags are optional. Within a given element, tags must appear in the order shown in the Reference.

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.

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.

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.

What We Do Connectbeam is an integrated set of social software applications for information sharing and team collaboration that is packaged and delivered as a turnkey Appliance. * Social Networking * Expertise Location * Collective Intelligence How We Do It Connectbeam provides to users a social search experience that includes the following integrated features: * Social Bookmarking & Tagging * Universal Content Tagging * Central Tag Repository * Integration with Existing IT Infrastructure * Plus additional features...

The intelligence aspect is that our tags have defined meanings, so when you tag a page, you are assigning it a definition rather than a simple word that could have multiple meanings. Having defined tags is what sets Zigtag apart from other social bookmarking web sites.

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.

LODr is a RDF-based (re-)tagging service, that allows people to weave their Web 2.0 tagged data into the Linked Data Web and provides a dedicated browsing interface.

Tagalus lets users define tags so that others can understand what they're talking about. Other users can vote on definitions and decide which best describes the given tag.