Group items tagged

The Semantic Web isn't just about putting data on the web. It is about making links, so that a person or machine can explore the web of data. With linked data, when you have some of it, you can find other, related, data. Like the web of hypertext, the web of data is constructed with documents on the web. However, unlike the web of hypertext, where links are relationships anchors in hypertext documents written in HTML, for data they links between arbitrary things described by RDF,. The URIs identify any kind of object or concept. But for HTML or RDF, the same expectations apply to make the web grow: 1. Use URIs as names for things 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information. 4. Include links to other URIs. so that they can discover more things.

"The semantic web proposes to inject machine meaningful data into the existing human language oriented web. As part of this effort, on the semantic web, URIs are used to identify entities. But there is currently no standard way to specify what it is that any given URI is to identify, or to whom, or when. Recent work in linguistics offers ideas for a solution to this lack. It focuses on the pragmatics of actual language use among ensembles of people. Also, the World Wide Web provides a set of technologies, in the form of socially constructed web sites, that could be employed to provide a solution. In this paper, I suggest how such socially constructed web sites could be used to address the problem of establishing common ground among a community of machines of the referent of a URI used on the semantic web. The result is a proposal to automate social meaning by creating societies of machines that share knowledge representations identified by URIs."

What tagging does point to convincingly is the social aspect of naming. In a given natural language, many sorts of identifiers, such as common words, are socially centralized. Other sorts of identifiers, such as proper names, are socially decentralized, varying from local context to local context. Black has noticed a correspondence between this socially grounded identification process and the use of socially constructed Web sites.

"URIs, a universal identification scheme, are different from human names insofar as they can provide the ability to reliably access the thing identified. URIs also can function to reference a non-accessible thing in a similar manner to how names function in natural language. There are two distinctly different relationships between names and things: access and reference. To confuse the two relations leads to underlying problems with Web architecture. Reference is by nature ambiguous in any language. So any attempts by Web architecture to make reference completely unambiguous will fail on the Web. Despite popular belief otherwise, making further ontological distinctions often leads to more ambiguity, not less. Contrary to appeals to Kripke for some sort of eternal and unique identification, reference on the Web uses descriptions and therefore there is no unambiguous resolution of reference. On the Web, what is needed is not just a simple redirection, but a uniform and logically consistent manner of associating descriptions with URIs that can be done in a number of practical ways that should be made consistent. "

A great review of the challenges that follow from using URIs for both access and reference

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 .

The Sematic Web Client Library represents the complete Semantic Web as a single RDF graph. The library enables applications to query this global graph using SPARQL- and find(SPO) queries. To answer queries, the library dynamically retrieves information from the Semantic Web by dereferencing HTTP URIs and by following rdfs:seeAlso links. The library is written in Java and is based on the Jena framework.

The Disco - Hyperdata Browser is a simple browser for navigating the Semantic Web as an unbound set of data sources. The browser renders all information, that it can find on the Semantic Web about a specific resource, as an HTML page. This resource description contains hyperlinks that allow you to navigate between resources. While you move from resource to resource, the browser dynamically retrieves information by dereferencing HTTP URIs and by following rdfs:seeAlso links.

Welcome to the National Center for Biomedical Ontology's BioPortal. BioPortal is a Web-based application for accessing and sharing biomedical ontologies. New features in BioPortal 2.0 include: * Full ontology navigation using Flash visualization * Web-service access to BioPortal content and capabilities, which enables developers to use our BioPortal services in their tools. * Ability to add Marginal Notes to classes in BioPortal ontologies, a feature that enables the community to comment on ontologies and to discuss their contents * Ability to create Point to Point Mappings between concepts in different BioPortal ontologies * Bulk export of ontology-to-ontology mappings in RDF format * Navigation of multiple ontologies, which enables users to have several ontologies opened simultaneously in different tabs in the user interface * URIs for all ontology content, which enable developers to access and share BioPortal content from their applications * Improved support through Protégé for ontologies in OWL format

Linked Data is a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data on the Semantic Web. The practice emphasizes Web access to data using existing Web technologies such as URIs and HTTP. It also emphasizes links between related Web resources.

# 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.

Named Graphs is the idea that having multiple RDF graphs in a single document/repository and naming them with URIs provides useful additional functionality built on top of the RDF Recommendations.

The GoodRelations ontology provides the vocabulary for annotating e-commerce offerings (1) to sell, lease, repair, dispose, and maintain commodity products and (2) to provide commodity services. GoodRelations allows describing the relationship between (1) Web resources, (2) offerings made by those Web resources, (3) legal entities, (4) prices, (5) terms and conditions, and the aforementioned ontologies for products and services (6). For more information, see http://purl.org/goodrelations/ Note: The base URI of GoodRelations has changed to http://purl.org/goodrelations/v1. Please make sure you are only using element identifiers in this namespace, e.g. http://purl.org/goodrelations/v1#BusinessEntity. T

"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