Some in the Semantic Technology community have pointed out that from a development perspective, Semantic Technologies are well suited for an agile approach to programming, and we will be discussing that idea more in future here at SemanticWeb.com. Today, however, we’re taking a look at some novel thoughts on agile development of a standard, thanks to guest contributor, Andreas Gebhard. He is Director, Editorial at Getty Images, and Board member of the IPTC.

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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If you are learning about the Semantic Web, one of the things you will hear is that the Semantic Web assumes the Open World. In this post, I will clarify the distinction between the Open World Assumption and the Closed World Assumption.

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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[Editor's Note: This guest post was provided by Tom Ilube, Managing Director, Callcredit Consumer Markets, and includes additional contributions from Rob Styles, Principal Technical Consultant, Callcredit)]

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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Recently, we published Yefim “Jeff” Zhuk’s article, “IT of the Future: Semantic Cloud Architecture.” The paper has been a very popular free download (available here).

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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In July of 2011, we published a series of articles, “From Business as Usual to Knowledge-Driven Architecture” by Yefim “Jeff” Zhuk. The series outlined enterprise IT of the future with integrated software and knowledge engineering, further expanding on ideas originally described in the book “Integration-ready Architecture and Design.”

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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Last week we reported here on the progress that Nova Spivack’s #OccupyTwitter petition was making in terms of attracting signatures, and on the petition’s request that Twitter clarify just what its intentions for the developer community are around its API. Many semantic and sentiment analysis applications, of course, depend heavily on the Twitter API.

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 recently pointed to a project called Hedgehog Street. This is the premise: “Hedgehogs travel around one mile every night through our parks and gardens in their quest to find enough food and a mate. If you have an enclosed garden you might be getting in the way of their plans. Hedgehogs have enough barriers to contend with such as roads and rivers that we can’t do much about. However we can make their life a little easier by removing the barriers within our control – for example making holes in or under our garden fences and walls for them to pass through. The gap need only be around 15cm in diameter and so should not affect your pets’ safety.” Read more

There has always been a misconception between the relationship of RDF and XML. The main difference: XML is a syntax while RDF is a data model.

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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If you would like your company to be considered for an interview please email editor[ at ]semanticweb[ dot ]com.

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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