Jack Park's Library tagged tags →  View Popular

The combination of classification, search enhancement, and contextual navigation delivers Findability.

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search tags tagging classification taxonomy findability

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.

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search tags web2.0 tagging tag tools searchengine

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

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twine tags tagging extreme tagging

TagMaps is a toolkit to visualize text (well, tags) geographically on a map.

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yahoo maps mashup map tags tagging web2.0 visual sensemaking sensemaking

# The primary intention of creating aTags is not the categorization of the document, but the representation of the key facts inside the document. Key facts in the biomedical domain might be, for example, “Protein A interacts with protein B” or “Overexpression of protein A in tissue B is the cause of disease C”.
# An aTag is comprised of a set of associated entities. The size of the set is arbitrary, but will typically lie between 2 and 5 entities. For example, the fact “Protein A binds to protein B” can be represented with an aTag comprising of the three entities “Protein A”, “Molecular interaction” and “Protein B”. Similarly, the fact “Overexpression of protein A in tissue B is the cause of disease C” can be represented with an aTag comprising of the four entities “Overexpression”, “Protein A”, “Tissue B” and “Disease C”.
# Each document or database entry can be described with an arbitrary number of such aTags. Each aTag can be associated with the relevant portions of text or data in a fine granularity.
#

The entities in an aTag are not simple strings, but resources that are part of ontologies and RDF/OWL-enabled databases. For example, “Protein A” and “Protein B” are resources that are defined in the UniProt database, whereas “Molecular Interaction” is a class in the branch of biological processes of the Gene Ontology. They are identified with their URIs.

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atags biordf bio-ontologies bioinformatics tags tagging

My topics of main interest are: 1) Associative Tags; 2) Agreement, Disagreement, discourse; 3) Corporate Semantic Web, 4) Are upper level ontologies/vocabularies not so bad after all?, 5) Cleaner schemas and ontologies

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vocamp vocabularies dialogue sensemaking tags tagging ontologies

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

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tagging web2.0 tags tools visualization tagcloud tag cloud sensemaking

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.

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semantic semanticweb tags faviki folksonomy semanic tags googlelanguageapi

You can integrate notitio.us with you browser. Just go to browser buttons page.

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notitio.us notitio bookmarklet tags treemap multibee

Efficient assembly of social semantic networks

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givealink tags tagging paper

An individual may create or reinforce a relationship
between two resources by applying a common tag or organizing them in a common folder. This has led to the exploration of techniques for building networks of resources, categories, and people using the social annotations. In order for these techniques to move from the lab to the real world, efficient building and maintenance of these potentially large networks remains a major obstacle. Methods for assembling and indexing these large networks will allow researchers to run more rigorous assessments of their proposed techniques. Toward this goal we explore an approach from the sparse matrix literature and apply it to our system, GiveALink.org. We also investigate distributing the assembly, allowing us to grow the network with the body of resources, annotations, and users.

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givealink tagging tags

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

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sirtel-2008 ceur-ws.org_vol-382 information retrieval social networks tags folksonomies

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.

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lodr moat linked_data sioc tags rdf web2.0 browser

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 .

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tagging semanticweb MOAT ontology semantic tag tags rdf php opensource

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)

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triplei folksonomies AndreasHotho tags similarity FolkRank structure PageRank

Welcome to Open Context, a free, open access resource for the electronic publication of primary field research from archaeology and related disciplines. Open Context provides an integrated framework for users to search, explore, analyze, compare and tag items from diverse field projects and collections.

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context archaeology web2.0 tags tagging database

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.

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tagging tags folksonomy

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.

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folksonomy tagging tags OpenContext

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

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sensemaking collaboration folksonomy tags playlist workflow

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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BernardoHuberman tagging research collaboration article folksonomy search tags bibliography