The paper is available at http://arxiv.org/PS_cache/arxiv/pdf/1103/1103.1255v1.pdf

Although mashups fully embrace the idea of customization on the Web, read-write is another story. The complexity of application development using Javascript makes it hard for average developers to create new mashups and to customize the existing ones. To solve this problem, several companies are developing tools for mashup creation that require little or no programming knowledge. These tools, exemplified by Yahoo! pipes, IBM’s QEDwiki and Google’s Mashup Editor, facilitate the selection of some number of RESTful Web services or other Web resources and chain them together by piping one service’s output into the next service’s input while filtering content and making slight format changes.

SA-REST is a microformat to add additional meta-data to (but not limited to) REST API descriptions in HTML and XHTML. Developers can directly embed meta-data from various models such an ontology, taxonomy or a tag cloud into their API descriptions. The embedded meta-data can be used to improve search (for example: perform faceted search for APIs), data mediation (in conjunction with XML annotation) as well as help in easier integration of services to create mashups.

SA-REST submission to W3C can be found here: http://www.w3.org/Submission/SA-REST/

More information about SA-REST can be found here: http://wiki.knoesis.org/index.php/SA-REST

For example, imagine you are watching a short documentary about clean water issues in India. As the video plays, adjacent windows will dynamically generate links to actions and media directly related to each scene. These could include organizations involved in clean water and sanitation, action campaigns related to water issues, relevant videos from YouTube, articles from research organizations, and the latest updates from news services and blogs.

The site will automatically create these links using semantic Web tools. Rather than simply scanning for keywords, this new technology also analyzes context and meaning to find the most relevant results from all the latest information and links available. It will also employ the Linked Data model, which assigns unique identifier tags to specific “entities” and organizes data in ways that can be read and understood by other applications. This allows the site to easily differentiate whether the word “Sahara” is being used to refer to the desert in North Africa or a hotel-casino in Las Vegas, and to communicate about the correct topic with other websites that hold related information.

By tapping into the semantic Web, the Linked Data model, and partner APIs, ViewChange.org is taking the latest advances in online technology and harnessing them for social change.

LinkTV created the working model for ViewChange.org by fostering relationships with semantic technology innovators such as OpenCalais and Zemanta. They are also actively experimenting with the open APIs of sites like YouTube and Kiva.org.

Link TV‘s long-term Web development partner Definition is responsible for building the platform and will utilize Freebase, Zemanta, OpenCalais, and other APIs help to provide an innovative customizable user experience.

In addition, ViewChange.org will feature an open API, inviting third-party sites and developers to create applications and pages fueled by our content. This API will feed into the Linked Data model. The code behind the platform will be made freely available.

Here you are the abstract of the paper:

Radiological images contain a wealth of information, such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats, enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

You can find the complete paper at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655990/?tool=pubmed.

SAPIENT stands for “Semantic Annotation of Papers: Interface & ENrichment Tool”. It is an annotation interface implemented as a web application, to help users annotate scientific papers in XML, sentence by sentence, with a set of concepts called General Scientific Concepts (GSCs, see http://ie-repository.jisc.ac.uk/88/). GSCs constitute the set of concepts essential for describing a scientific investigation. However, SAPIENT can also be used in conjunction with other annotation schemas to annotate papers in XML sentence by sentence.SAPIENT also incorporates Oscar3 functionality, allowing the automatic annotation of chemical named entities.

SAPIENT includes the so called SAPIENT Sentence Splitter (SSSplit). SSSplit is an XML-aware sentence splitter which preserves XML markup and identifies sentences through the addition of in-line markup. The reason for developing our own sentence splitter was that sentence splitters widely available could not handle XML properly. The XML markup contains useful information about the document structure and formatting in the form of inline tags, which is important for determining the logical structure of the paper.

SSSplit has been written in the platform-independent Java language (version 1.6), based on and extending open source Perl code for handling plain text. In order to make our sentence splitter XML aware, we translated the Perl regular expression rules into Java and modifed them to make them compatible with the SciXML schema.

Via: http://zillman.blogspot.com/2009/11/sapient-semantic-annotation-of-papers.html

Basically:

SMW+ is ontoprise’s enterprise semantic wiki that makes it easy for your team to cope with knowledge-intensive processes and to exploit the knowledge locked in content.

SMW+ combines a wiki’s social authoring approach with proven semantic technology.

This yields a powerful tool which is business-ready for your organization to:

• Create and share semantically tagged project documentation,
• Create and publish user manuals and training materials which are enriched with semantic annotations for precise retrieval, or
• Check semantically tagged content for consistency.

Todays’ webinar is titled: “Basic semantic features of SMW+”.

You can find all the information about this webinar at: http://smwforum.ontoprise.com/smwforum/index.php/Webinars/Schedule_of_webinars