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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With this week’s unveiling of the Knowledge Graph, Google has taken another step toward realising the potential of their Metaweb acquisition. The company has also clearly demonstrated its continued enthusiasm for delivering additional user value without requiring changes in user behaviour (well, except that those of us outside the US have to remember to use google.com and not our local version, if we want to try this out).

For those who don’t remember, Metaweb was one of those companies that got people excited about the potential for semantic technologies to hit the big time. Founded way back in 2005, Metaweb attracted almost $60Million in investment for their “open, shared database of the world’s knowledge” (Freebase) before disappearing inside Google in 2010.

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Today, May 9, 2012 is Global Accessibility Awareness Day (#GAAD).

According to the US Centers for Disease Control and Prevention, “Today, about 50 million Americans, or 1 in 5 people, are living with at least one disability, and most Americans will experience a disability some time during the course of their lives.” In other parts of the world, this number may be significantly higher.

In the interest of full disclosure, Joe Devon is a personal friend of mine, and I must admit that if he were not, I likely wouldn’t have seen his blog post or explored the issues of accessibility as deeply as I have in recent weeks. But I have been exploring, and I’ve been surprised at what I’ve found. In my opinion, Semantic Technology and Assistive Technology are a natural fit for one another, but there seems to be very little discussion or work around the intersection of the two. I have looked, but have not found much collaboration between the two communities. I have also found few individuals who possess much knowledge about both Semantic Tech and Assistive Tech. Of course, if I’ve missed something, please let me know in the comments!

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While sentiment analysis continues to generate a lot of press, it is not clear how much real value organizations are deriving from it.  One reason for that is that the standard approach to sentiment has been mostly statistical and/or long lists of sentiment terms.  However, if you add in other, advanced text analytics capabilities such as auto-categorization using advanced operators, you can not only develop more sophisticated sentiment analysis, you can also develop a whole new class of applications that either enhance and/or go beyond simple sentiment analysis.

These advanced operators include such commands as DEST_6 (count two words as a positive indicator only if they are with 6 words of each other) or SENT (only count words in the same sentence).

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There are a number of ways to structure your data to make it more relevant to search engines, as well as social media sites. As an e-commerce retailer it is important to understand which of these standards you should consider including in your website. You should take some time to ensure you are implementing semantic markup, and doing it correctly. It has the power to better inform potential customers with upfront knowledge prior to landing on your site. Customers can see product reviews, pricing and stock information, and even images before clicking through to your website. This can lead to increased click-through rates, improve conversions, and generally enhance your SEO objectives.

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NOTE: This post is provided by guest author, Mr. Dennis E. Wisnosky, Chief Technical Officer and Chief Architect, Business Mission Area, U.S. Department of Defense. Dennis will be delivering a Special Presentation, “The Enterprise Information Web: Analytics, Efficiency and Security” at the June SemTechBiz Conference.

Semantic Technology brings a number of unique capabilities to data stores and applications.  These capabilities evidence themselves both at the user interaction level, in what users can do with and expect from Semantic technologies; and at the system level, in terms of things applications can do internally without rework or recoding.  Semantic Technology, based upon W3C standards, provides capabilities significantly beyond those of proprietary approaches based on technologies that were founded a half century earlier.

1. User Interaction Capabilities

Access to Meaning

Semantic Technology is based upon the development of the ontology of a particular domain.   That is, “what do I need to know to have an unambiguous understanding of a particular thing, organization, subject, etc.?”  This knowing is based upon precise understanding of the meaning of words used in the domain.  A Semantic-Technology-based application depends on and provides a user with access to the defined meaning of the terms—the vocabulary, the words—used in the application.  This means access to a human-only readable definition, such as one found in a dictionary, and access to the formalized definition found in the ontology that frames the system which executes the application.  Such access should be presented in a human consumable form, and is one of the areas in which various formalisms such as Controlled Natural Language (CNL) are useful for translating technical forms of ontologies, such as the Web Ontology Language (OWL) , a W3C standard, to provide a human consumable form.

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“Now that we’ve gotten this validation, we’ll charge full-steam ahead with our bigger vision for pragmatic Linked Big Open Data in NYC,” says Ontodia co-founder Joel Natividad.

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“We are working to rethink and redesign the various ways in which our customers and clients interact with money. We will collaborate with IBM to explore how we can use the Watson technology to provide our customers with new, secure services designed around their increasingly digital and mobile lives,” said Don Callahan, Citi’s Chief Administrative Officer and Chief Operations & Technology Officer in the press statement. The plan is to use Watson’s NLP capabilities for analyzing human-language questions and drawing upon its interpretations of the query to derive potential answers that it then tests, validates, and scores, to help financial reps sort out options, opportunities and risks targeted to a consumer’s individual circumstances.

Earlier this year, IBM paired up with WellPoint in a strategic partnership in another vertical sector, health care, with the goal of using Watson’s DeepQA to help physicians improve treatment for oncology patients by assessing medical evidence and personal case data to deliver probability-based treatment options.

But financial services has always been discussed as an industry that could benefit from Watson’s brainpower, too. In a video here, Dr. Carl Abrams of IBM Research, Financial Services, says that, “The currency of financial services is information, and the ability to semantically analyze that, to extract from that what the meaning is, and then take that meaning and apply it to something is simply becoming a level playing field.”

Financial services executive Jay Dweck, formerly global head of strategy and technology at Morgan Stanly, notes in the video that data in the sector is growing about 70 percent a year, and that having this much data requires a large range and variety of tools to be able to extract real knowledge from it. “You could put together the logical connections among the disparate pieces of information,” he said.

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According to Purdy, more people are accessing Facebook on the mobile web than from its top native apps combined, and the game is on to help developers conquer the challenges of building for that community. One of those challenges is app discovery. At the Mobile World Congress on Monday, the company announced that it’s continuing to address the first issue with plans to extend to native Android apps the ability for Facebook’s 425 million mobile app users to discover them through Open Graph connections.

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Dr. Eric Charton, Ph.D, MSc at École Polytechnique de Montréal, is project leader and author of the Wikimeta code. The NLGbAse project was conducted by Charton at the University of Avignon as part of his Ph.D. Thesis.  The Semantic Web Blog recently hosted an email discussion with him to learn more about the Wikimeta architecture and its evolution.

The Semantic Web Blog: Tell us about the NLGBase project and Wikimeta’s relationship to it.

Charton: NLGbAse is an ontology extracted from Wikipedia. It is used in Wikimeta as a resource for semantic disambiguation. For each Wikipedia document (aka Semantic Concept), NLGbAse provides various ways of word-writing (for example, “General Motors” can be written “GM Company”, “GM”, “General Motors Corp” and so on), used for detection.

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The application was built with fluid Operations’ Information Workbench, which is a platform for building self-service Linked Data cloud apps; the company also provides the eCloudManager Product Suite, for public and private cloud management. With Information Workbench, users can get over some of the challenges of making Linked Data useable, such as automatically discovering and integrating data sources, dealing with heterogeneity in data sets and access, and planning end-user oriented interfaces and interaction paradigms, says Peter Haase,  a senior architect at fluid Operations, who will be speaking about Linked Data-as-a-service at SemTech Berlin.

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Digital Reasoning

The company has quickly added support for new languages such as Arabic, traditional and simplified Chinese, Farsi and Urdu (with more languages on the way) to Synthesys using the tool. The tool gets the software up to speed on each one in just a few weeks by teaching it the grammatical structure and then letting it go off and figure out what the words mean for its work of transforming unstructured (and structured) data into the underlying facts, entities, relationships, and associated terms.

“In the same way we teach it languages you may have a data set that is highly scientific, for example, and this tool essentially makes it easier for our customers to make Synthesys even more accurate for that specific set of data,” says Dave Danielson, VP of marketing.

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“That is motivation to apply semantic web technologies because you no longer are working in closed walls where you build your own schema and database model, but you need a flexible semantic model that easily integrates with others,” says Paschke.

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Silk, which The Semantic Web Blog previously has covered here and here, now is in the process of testing its WYSIWYG Silk Editor with a select user set, and is slowly inviting more interested parties to get involved. It expects to release it publicly soon. The simplicity of the Silk Editor, says Sander Koppelaar, head of business development, is that it looks very much like familiar environments – think a graphical Wiki – while supporting tagging information on a page, such as the population or capital of Amsterdam, if that were the subject.

“That way you first create pages that are very handy for users because they are built for humans, containing text and images you’d see on a normal web site,” he says. “But more or less without noticing it you build on your data model and can start to use that to create the great overviews and answer actual questions about the data.”

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