Introducing the Calais Web service (4.0) A Thomson Reuters initiative designed to power next generation publishing solutions A free API that anyone can use at www.OpenCalais.com The fastest way to categorize & metatag the people, places, companies, facts and events in your content An easy way to connect to the open data sources in the Linked Data Cloud , including Wikipedia, DBPedia, Shopping.com, the Internet Movie Database (IMDB) and more

A Thomson Reuters initiative designed to power next generation publishing solutions

A free API that anyone can use at www.OpenCalais.com

The fastest way to categorize & metatag the people, places, companies, facts and events in your content

An easy way to connect to the open data sources in the Linked Data Cloud , including Wikipedia, DBPedia, Shopping.com, the Internet Movie Database (IMDB) and more

Why? Tagging text is costly and time-consuming We help in areas where: The economics don’t support metadata creation The value of metadata is potentially high The value of aggregated metadata is potentially extremely high Seconds Years Seconds Years Tweets Blogs News Scient. Pubs Great Novels Latency Shelf Life

We help in areas where:

The economics don’t support metadata creation

The value of metadata is potentially high

The value of aggregated metadata is potentially extremely high

Why? What Calais can help you do… Automate: Automatically tag the people, places, companies, facts and events in your content to increase its value and interoperability. Enhance: Enrich your content with open data from Wikipedia, the Internet Movie Database (IMDB), Shopping.com and more. Engage: Optimize your user experience, increase engagement and drive repeat visits with topic pages, personalized filtering and real-time alerts. Extend: Increase your syndication to next generation search engines, news aggregators, ‘related stories’ applications and others. Connect: Enter the emerging Linked Content Economy. Compete in a rapidly evolving ecosystem of enriched and interconnected content.

Automate: Automatically tag the people, places, companies, facts and events in your content to increase its value and interoperability.

Enhance: Enrich your content with open data from Wikipedia, the Internet Movie Database (IMDB), Shopping.com and more.

Engage: Optimize your user experience, increase engagement and drive repeat visits with topic pages, personalized filtering and real-time alerts.

Extend: Increase your syndication to next generation search engines, news aggregators, ‘related stories’ applications and others.

Connect: Enter the emerging Linked Content Economy. Compete in a rapidly evolving ecosystem of enriched and interconnected content.

How it works: A semantic metadata generation service that extracts entities, facts and events from unstructured text Creates linkages from extracted entities to linked data ecosystem Provides a transportation layer for rich semantic metadata from producers to consumers

A semantic metadata generation service that extracts entities, facts and events from unstructured text

Creates linkages from extracted entities to linked data ecosystem

Provides a transportation layer for rich semantic metadata from producers to consumers

<Acquisition offset=&quot;494&quot; length=&quot;130&quot;> <Company_Acquirer>Reuters</Company_Acquirer> <Company_Acquired>ClearForest Ltd.</Company_Acquired> <Status>Planned</Status> </Acquisition> <Company>Reuters</Company> <Company>ClearForest Ltd.</Company> Reuters Announced the Acquisition of ClearForest New York - April 30, 2007 Reuters, the global information company, has entered into an agreement to acquire all of the outstanding shares of ClearForest Ltd., a privately held provider of Text Analytics solutions, whose tagging platform and analytical products allow clients to derive precise business information from huge amounts of textual content. ClearForest has received sufficient shareholder approval to complete the transaction, which is expected to close in approximately 30 days, subject to customary closing conditions. The financial terms were not disclosed. Reuters plans to retain and continue to work with the existing management team and their highly skilled workforces in the US and Israel. It also plans to continue to support existing products and customers. Reuters believes that search will be a pivotal element to the future of how financial information is sourced and consumed. As part of its drive into this space, Reuters has created a new strategic group and appointed Gerry Campbell, who will oversee the integration of ClearForest and drive this innovation. <Product>Text Analytic Solution </Product> <Company>ClearForest Ltd.</Company> <Company>Reuters</Company> <Country>United States</Country> <Country>Israel</Country> <Company>Reuters</Company> <Person>Gerry Campbell</Person> <ManagementChange offset=&quot;2789&quot; length=&quot;92&quot;> <Person>Gerry Campbell</Person> <Company>Reuters</Company> <Action>Enters</Position> </ManagementChange> Text markup by Calais <Topic>M&A</Topic>

NEW! NEW ! The Linked Data Cloud with new OpenCalais and Thomson Reuters information assets

Unstructured Text Calais extracts entities, facts and events Metadata returned to the user with keys Keys provide access to the Calais Linked Data cloud Which provides information and other Linked Data pointers To a range of open and partner Linked data assets, including Thomson Reuters 1 2 3 4 5 6 The Process

Quick online demo Copy and paste the text of a business news article into the viewer here: http://viewer.opencalais.com and press submit. The article is sent to the Calais engine which tags the content and returns it, marked-up. The tags appear on the left hand rail, and you can click on the plus (+) sign to see the tags expand. (Note that the Calais Viewer is not the Calais service. It is merely a demonstration of how the service works.) Since we are now on Calais 4.0, you can also use the viewer to see the Linked Data assets related to the tags Calais returns. For example, here is the Calais summary page for IBM: http://d.opencalais.com/er/company/ralg-tr1r/9e3f6c34-aa6b-3a3b-b221-a07aa7933633.html And here is the summary page for IBM in DBPedia (the Wikipedia translated into computer language): http://dbpedia.org/page/IBM

Copy and paste the text of a business news article into the viewer here: http://viewer.opencalais.com and press submit. The article is sent to the Calais engine which tags the content and returns it, marked-up.

The tags appear on the left hand rail, and you can click on the plus (+) sign to see the tags expand. (Note that the Calais Viewer is not the Calais service. It is merely a demonstration of how the service works.)

Since we are now on Calais 4.0, you can also use the viewer to see the Linked Data assets related to the tags Calais returns.

For example, here is the Calais summary page for IBM: http://d.opencalais.com/er/company/ralg-tr1r/9e3f6c34-aa6b-3a3b-b221-a07aa7933633.html

And here is the summary page for IBM in DBPedia (the Wikipedia translated into computer language): http://dbpedia.org/page/IBM

Calais progress to date Launched in late January, 2008 Already, 9,500 developers have joined OpenCalais.com 1-3 million content ‘transactions’ per day Delivered four major update releases Lots of interesting apps & integrations Drupal WordPress Afresco Many others

Launched in late January, 2008

Already, 9,500 developers have joined OpenCalais.com

1-3 million content ‘transactions’ per day

Delivered four major update releases

Lots of interesting apps & integrations

Drupal

WordPress

Afresco

Many others

What’s coming French language support – DONE! Linked Data Integration – DONE! Spanish language support – in process… Social tags – simple topical tags for “aboutness” ? Tell us what you’d like to see @ OpenCalais.com

French language support – DONE!

Linked Data Integration – DONE!

Spanish language support – in process…

Social tags – simple topical tags for “aboutness”

? Tell us what you’d like to see @ OpenCalais.com

Sample Calais Applications

Example: The Mail & Guardian Online, South African Newspaper Using Calais to metatag new and historical articles, and: Build an index or topics A-Z Pull out automatic related articles or pictures Create news alerts on companies or people Pull up maps for the countries named in articles Predict readers’ interests based on browsing habits Create tag clouds to show popular subjects, people, etc. Using Calais to optimize search and navigation; drive consumer engagement

Using Calais to metatag new and historical articles, and:

Build an index or topics A-Z

Pull out automatic related articles or pictures

Create news alerts on companies or people

Pull up maps for the countries named in articles

Predict readers’ interests based on browsing habits

Create tag clouds to show popular subjects, people, etc.

Example: Gist - today’s news filtered by people, places & events GIST uses Calais to prioritize stories, rank newsmakers & reveal trends / reader demand. It automatically aggregates multiple news sources and slots them into topic.

Example: The Powerhouse Museum in Sydney Using Calais to tag historical archives & using tags as search terms