We caught up with Gebhard at the recent Semantic Technology & Business Conference in New York, where he initially shared this idea with us.

He has expanded on these ideas in a post on the Getty Images blog. As Gebhard says, “I want to tell you the story of how we got there in just about a year — tremendously fast, in the world of standards.”

We re-print the post in its entirety below with thanks to the author and Getty Images.

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The Closed World Assumption (CWA) is the assumption that what is not known to be true must be false.

The Open World Assumption (OWA) is the opposite. In other words, it is the assumption that what is not known to be true is simply unknown.

Consider the following statement: “Juan is a citizen of the USA.” Now, what if we were to ask “Is Juan a citizen of Colombia?” Under a CWA, the answer is no. Under the OWA, it is I don’t know.

When do CWA and OWA apply?

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Big Graph Data Panelists (L to R): Mike Stonebraker, John Giannandrea, Bryan Thompson, Tim Berners- Lee, Frank van Harmelen

Last week, the 11th International Semantic Web Conference (ISWC 2012) took place in Boston. It was an exciting week to learn about the advances of the Semantic Web and current applications.

The first two days, Sunday November 11 and Monday November 12, consisted of 18 workshops and 8 tutorials. The following three days (Tuesday November 13 – Thursday November 15) consisted of keynotes, presentation of academic and in-use papers, the Big Graph Data Panel and industry presentations. It is basically impossible to attend all the interesting presentations. Therefore, I am going to try my best to summarize and offer links to everything that I can.

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If you intend to introduce Big Data and Linked Data approaches to your company you may wish to learn from the way pygmies hunt elephants.

Forty years ago my father took me on safari in remote parts of Kenya. We ended up slightly off the beaten track, in a pygmy village. They were very welcoming and one evening as we sat around chatting some of the young men told us how they hunt elephants. Pygmies, I am reliably informed, approach this formidable task in three easy steps.

Step 1: A team of two pygmies roll around in elephant dung, so that they can sneak up on said elephant without being detected;

Step 2: Pygmy A (let us call him “Joe”) climbs on Pygmy B’s (“Fred”) shoulders, underneath the elephant (“Nelly”). Joe uses a short, sharp spear to attack the soft underbelly and aim directly for Nelly’s heart. Then they run. Fast.

Step 3: Joe and Fred return to the village, carrying an ear each, and are received as heroes as the whole village feasts for weeks.

Mind you, if Joe and Fred return covered in dung and without ears then they are given pretty short shrift by their fellow village, let me tell you! There is also the minor risk of being stomped on. But otherwise, it’s a foolproof plan.

This is precisely the approach that Rob Styles (Pygmy A) and I (Pygmy B) are taking in introducing Big Data concepts at Callcredit, the UK credit reference agency. Well, perhaps not precisely. But let’s see how far I can push this ridiculous analogy.

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One of the readers, Lev Gorodinski (CTO, EPaySpot), approached Jeff directly with some questions and the two engaged in a conversation filled with insights that they wanted to share with our readers. They are kindly allowing us to republish the thread in its entirety.

Lev Gorodinski: I’ve read the article and have some bigger picture questions and comments which likely warrant several discussions. Overall, I am interested in methodologies which aim to bridge the gap between knowledge and its technical manifestation and am therefore interested in the subject matter.

The term “sandbox” in BASE may create the impression that it isn’t a production level system. I think that both the goal of BASE to “Decrease the number of manual operations required for business changes” and its notion of a “playground” are essential to making it ready for production. This will allow an agile and iterative development and exploration process.

Jeff Zhuk: The primary purpose is setting a common ground where business analysts and developers can collaborate on real business tasks. For some companies this ground can serve in production and other companies might feel more comfortable to use it as a playground for safe development and testing before copying to production. The role of the playground will be growing without growing maintenance cost.
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Today, we are pleased to offer Jeff’s latest article as a 27-page PDF file. In this new article, he focuses on the process of transitioning from IT architectures of today to Semantic Cloud Architecture with very practical “baby steps” — steps which require minimum upfront investment. The emphasis of this article is on collaborative work of business and enterprise architects with the Business Architecture Sandbox for Enterprise, (BASE) that was demonstrated at the 2012 Semantic Tech and Business Conference –San Francisco.

Zhuk says, “The discussed approach is gradually shifting the focus of IT from technology to information by standardizing business event processing, placing the seeds of semantic technology in the current business ground, and establishing a self-sustaining process of transformation to semantic cloud architecture. The article provides the context and speaks technical details for this transition.”

Read/Download the full paper (registration required)

As a teaser, here is the beginning of the article and Section Headings…

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Well, the end of last week saw a blog post from Michael Sippey of Twitter that provided some more information on the API issue. He wrote:

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Ross Spencer of the UK National Archives

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RDF has several syntaxes (Turtle, N3, etc) and XML is one of those (known as RDF/XML). Actually, RDF/XML is the only W3C standard syntax for RDF (Currently, there is Last Call on Turtle, a new W3C standard syntax for RDF). Therefore, comparing XML and RDF is like comparing apples with oranges. What can be compared is their data models. The RDF data model is a graph while the XML data model is a tree.

Comparing RDF with XML

Joshua Tauberer has an excellent comparison between RDF and XML, which I recommend. Two advantages of RDF are highlighted: flexibility of the data model and use of URIs as global unique identifiers.

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In this segment of our “Innovation Spotlight” we spoke with Elliot Turner (@eturner303), the founder and CEO of AlchemyAPI.com. AlchemyAPI’s cloud-based platform processes around 2.5 billion requests per month. Elliot describes how their API helps companies with sentiment analysis, entity extraction, linked data, text mining, and keyword extraction.

Sean: Hi Elliot, thanks for joining us, how did AlchemyAPI get started?

Elliot: AlchemyAPI was founded in 2005 and in the past seven years has become one of the most widely used semantic analysis APIs, processing billions of transactions monthly for customers across dozens of countries.

I am the Founder and CEO and a serial entrepreneur who comes from the information security space.  My previous company built and sold high-speed network security appliances. After it was acquired, I started AlchemyAPI to focus on the problem of understanding natural human language and written communications.

Sean: Can you describe how your API works? What does it allow your customers to accomplish?

Elliot: Customers submit content via a cloud-based API, and AlchemyAPI analyzes that information in real-time, transforming opaque blobs of text into structured data that can be used to drive a number of business functions. The service is capable of processing thousands of customer transactions every second, enabling our customers to perform large-scale text analysis and content analytics without significant capital investment.

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“What we have found with this project is… the capacity to take value out of open data is very limited.”

With the abatement of the media buzz surrounding open data since the first International Open Government Data Conference (IOGDC) was held in November 2011, it would be easy to believe that the task of opening up government data for public consumption is a fait accompli.  Most of the discussion at this year’s IOGDC conference, held July 10-12, centered on the advantages and roadblocks to creating an open data ecosystem within government, and the need to establish the right mix of policies to promote a culture of openness and sharing both within and between government agencies and externally with journalists, civil society, and the public at large.   According to these metrics the open government data movement has much to celebrate:  1,022,787 datasets from 192 catalogs in 24 languages representing 43 countries and international organizations.

The looming questions about the utility of open government data make it clear, however, that the movement is still in its early stages.    Much remains to be done to to provide usable, reliable, machine-readable and valuable government data to the public.

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Looking at the results (which have been pasted at the end of this article for convenience), I’m struck once again by the diversity of points of view in the Semantic Web community on what the key value of its technology really is. Over at Semantic University we summarized what we believe to be the two dominant camps (summary: AI-centric and flexible data management-centric) in the Semantic Web world, and the results of this exercise illustrate clearly that there are many nuances within those camps.

I’ll go into some highlights, but I think the why is still missing in many cases.  It’s the classic features-not-value predicament that plagues technologists and frustrates technology marketers.  We’re doing better, but we can and must do better still.

Data Flexibility: Data Integration

In terms of data flexibility, there are a number of themes that kept popping up.  Aaron Bradley first called out “cheaper enterprise data integration, and Lee Feigenbaum concurred by stating, “The Semantic Web is the only scalable approach for integrating diverse data.”  Another one I liked about data integration was from Abir: “Semantic Web technologies can make it possible to have true bottom-up web-scale automatic information integration.”

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So far we’ve looked at the “cutting edge” of dynamic semantic publishing (BBC Olympics) and we’ve seen what tools large publishers such as the New York Times, Associated Press, and Agence France Press are using to semantically annotate their content.

And we’ve learned how semantic systems help publishers “Do More With Less”- that is, automate a lot of the work organizing content and identifying key concepts, entities, and subjects- and “Do More With More” – combine their content with related linked open data and present it in different contexts.

You may still be asking at this point, “What makes this so novel and cool?  We know that semantic tools save time and resources.  And some people say semantic publishing is about search optimization, especially after the arrival of Google’s Knowledge Graph.  But the implications of semantic publishing are about oh so much more than search.    What semantic systems are really designed for, to use the phrase attributed to Don Turnbull, is “information discovery” and, if semantic standards and tools are widely adopted in the publishing world, this could have huge implications for content and data syndication.

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Andraz Tori is the Owner and Chief Technology Officer at Zemanta, a tool that uses natural language processing (NLP) to extract entities within the text of a blog and enrich it with related media and articles from Zemanta’s broad user base.    This interview was conducted for Part 3 of the series “Dynamic Semantic Publishing for Beginners.”

Q. Although the term “Dynamic Semantic Publishing” appears to have come out of the BBC’s coverage of the 2010 World Cup, it looks as though Zemanta has been applying many of the same principles on behalf of smaller publishers since 2008.  Would you characterize it this way, or do you think that Zemanta is a more limited service with specific and targeted uses, while the platform built by BBC is its own semantic ecosystem?  How broadly should we define Dynamic Semantic Publishing?

A. What Zemanta does is empower the writer through semantic technologies. It’s like having an exoskeleton that gives you superpowers as an author. But Zemanta does not affect the post after it was written.   On the other hand dynamic semantic publishing is based on the premise of bringing together web pages piece-meal from a semantic database, usually in real time.

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There’s been discussion on chat boards, for instance, about just who’s gotten access and who hasn’t. In a discussion with a representative from Google, The Semantic Web blog has learned that, like many other new Google services, the roll-out is gradual, in order to ensure the system is handling new functions well. First-come, first-served are those who are signed into Google – but then again, not everyone who is signed in. But the plan is to have everyone who’s signed in on board over the next few days, the rep says; so if you are and don’t have it yet, it should be hitting your browser shortly. Those not signed into Google accounts probably have a week or two of a wait left. So far, the rep said that things have been pretty smooth, so Google’s going at the pace it was hoping to.

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