Take a Kickstarter project dubbed Gooey Search – it’s trying to get funding of at least $125,000 by June 8 for its consumer-facing technology, based on latent semantic analysis (LSA). It has as its goal delivering the best and most accurate Google search results in what it calls a Gooey Graph real-time diagram of discovered network concepts, while keeping user privacy intact.

With the recent announcement of Google’s Knowledge Graph, do we need another way to probe the leading search engine? Ed Heinbockel, founder, president and CEO of Visual Purple, is betting we do. “To me [what Google’s done] validates the approach we’ve gone down in terms of visualizing and letting you navigate search. It’s the same direction. The question is can we provide value to that equation,” he says.

Where he sees the opportunity: “What drives us a lot is that we’re very concerned about the direction that privacy and the Internet are going,” he says. “And we think the quality of the results they give back to users isn’t in the order it should be.”

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First, a little bit about the bigger picture.  “What can happen when you go all the way to semantic, model-driven, knowledge computing [is that] … it changes the game for development,” says Mills Davis, managing director of Project 10X and one of the session’s presenters. “It enables new categories of capabilities and levels of user experience (think SIRI for the rest of us). It brings about quantum changes in all stages of lifecycle value. It enables cost-effective strategy-driven approaches to enterprise transformation. This last sentence is worth some reflection.”

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The organization this month published a new electronic edition of the Code of Federal Regulations (CFR), which contains a bevy of rules across 50 titles that impact nearly all areas of American business. Work underway at LII, dubbed the Linked Legal Data project, seeks to apply Linked Open Data principles enhances access to the CFR, with capabilities such as  being able to search its Title 21 Food and Drugs database using brand names for drugs (such as Tylenol), and receiving the generic name for the drug (acetaminophen) as a suggested term. “You cannot look for regulatory information on Tylenol in the CFR because Tylenol will never be there,” says Dr. Núria Casellas, who is a visiting scholar at the LII spearheading work on the project. “That is a brand name. What you actually want to look for are components, such as acetaminophen.”

While the general citizenry might find reasons to leverage the fruits of this effort, businesses that must comply with these requirements are a more likely target – not just the lawyers and paralegals, but those responsible for tasks, for example, such as storing and caring for products their company exports or imports, including understanding the safety regulations that apply to it. The Tylenol-acetaminophen example, she says, is very interesting because it showcases how using the wrong word or the incorrect approach can hamper a company from being able to find the relevant regulatory or safety information it needs to take into consideration.

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Google announced they’re rolling out new enhancements to their search technology and they’re calling it the “Knowledge Graph.”  For those involved in the Semantic Web Google’s “Knowledge Graph” is nothing new. After watching the video, and reading through the announcements, the Google engineers are giving the impression, to those familiar with this field, that they have created something new and innovative.

While it ‘s commendable that Google is improving search it’s interesting to note the direct translations of Google’s “new language” to the existing semantic web vocabulary. Normally engineers and researchers quote, or at least reference, the original sources of their ideas. One can’t help but notice that the semantic web isn’t mentioned in any of Google’s announcements. After watching the different reactions from the semantic web community I found that many took notice of the language Google used and how the ideas from the semantic web were repackaged as “new” and discovered by Google.

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Over at the BBC, for example, the 2012 Olympics site accompanies a completely redesigned BBC Sports site, both based on technology including Fluid Operations’ Information Workbench to support the editorial process for the BBC’s Dynamic Semantic Publishing strategy, from authoring and curation to publishing of ontology and instance data following an editorial workflow. The BBC environment since the World Cup also has been updated to use the MarkLogic document store for managing rapidly changing statistics, navigation and ultimately all content objects, as lead architect Jem Rayfield described it in this blog posting. Today, the triple store that’s been behind the BBC’s past work is extended to cover every team, athlete, venue, discipline, country and so on, Rayfield told The Semantic Web Blog.

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Semantic technology is helping the company reach its goal. “A big problem is that data about food is very messy,” says Penev. “It’s hard to find something, what you find often contradicts other information of what is good for you and what the calories are. So we set out to solve that problem. We played around with different approaches but settled on using semantic technology.”

The confusion arises in part from the fact that recipe sites themselves usually just hire services to calculate nutritional data. But that may lead to mistakes when calculations aren’t undertaken with exactitude — substituting white cream for heavy cream nutritional details changes the whole profile of the recipe, he says.

So, what is that right semantic stuff? One piece of it is that, in conjunction with Ontotext, Edamam built a food ontology. An ontology can be the foundation for a lot of things, such as extracting the knowledge of the chemical composition of a particular recipe and thus inferring its flavor and texture. And Edamam means to grow its own to include various datasets such as chemical data (for flavor and texture), geolocation (for local and seasonal recipes), product data (for e-commerce). and more.

But initially, it’s taken the simple approach, with the core of the ontology focused around classifying ingredients, nutrients and food. “We have started with the simplest ontology and focused on the most common use case — mobile recipe search,” he says.

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Coming up now is a complete integration of the NetBase technology into SAP’s Social On Demand customer relationship management (CRM) console. “Having access to social data is becoming critical to every part of the organization,” says NetBase chief marketing officer Lisa Joy Rosner. So, “social media [becomes] just one more data point” for which the enterprise must account.

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“A lot of the sites during the private beta were, well… private, so we can’t go into details about those,” says Sander Koppelaar, head of operations. Countries of the World, with all United Nations member state information, is one public Silk-powered demo web site for those who’d like to explore one. Generally speaking, he says there’s been a wide variety of use cases, ranging from professional publishers and data journalists to businesses and even personal use. “Publishers have used Silk to interpret data sets such as deadly traffic accidents, house sales and MBA rankings.

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At this week’s Sentiment Analysis symposium in New York City, Banafsheh Ghassemi, the American Red Cross vice president of marketing, e-CRM, and customer experience, pointed out some reasons why. “It’s a brave new world for those of us in the non-profit world,” she said. While charitable organizations don’t like to use the word competition, because they’re all working for the greater good, the number of non-profits angling for contributors’ dollars, time, or other resources, has grown by 60 percent in recent times. And there are even for-profit organizations doing some of the same things the Red Cross itself does, such as blood collection. “We still have to attract your attention for your dollar, time, and even the physical part of you that is blood,” she says.

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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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With its software, originally discussed here, qualitative, unstructured information is turned into a quantitative data set allowing users – machines and humans – to quickly analyze thousands of news stories in less time than it takes to read a single headline, as Thomson Reuters describes it. It uses natural language processing technology to get to the end game, which is to forecast financial market response from news and social media sentiment. Some 82 fields of metadata come into play for automating the analysis of news content. That encompasses sentiment down through to the degree of positive, negative or neutral expressions and how individual companies mentioned in a piece fare in those respects – rather than just the tone of the piece at large. “The computational linguistics system measures the author’s tone as positive or negative on any given entity, which is important and the harder part of it,” Brown said. Other fields include, for example, relevance, genre, intensity of news flow, and more.

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Turns out, RDF is not at play here. But natural language processing certainly factors in, albeit from the perspective of information extraction and being almost entirely machine-learning based rather than deep-parsing oriented. The service’s technology is influenced by the expert machine-reading NLP work being done at the University of Washington, where Wavii advisor Oren Etzioni is a professor of computer science.

But Wavii CEO and founder Adrian Aoun can credit growing up in a household where his father was a linguist — a student of Noam Chomsky – for originally sparking his interest in how language works. Whenever his dad would have a debate about a language construct with his fellow MIT buddies, he says, “they’d turn to me and say which one sounds better….The irony is that they’re arguing over the rules, but they acknowledge the right answer is whatever humans do.”

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DeWeese has been with the company since 2007, putting in place a sophisticated semantic system for auto-tagging and indexing content using natural language processing and controlled vocabularies, and leveraging its taxonomy for projects such as providing advanced search functionality. Thanks to building a collaborative communication channel with Tribune executives, producers, and editors, “now I actually am in meetings with executives who say how exciting it is that we now can be part of a community of people applying semantic technologies to content,” he says. “The other day I was at a meeting where a top executive used the word ontology all the time. I kept smiling and later I thanked her.”

Closely engaging with his business customers also is helping make it possible to push the semantic vision further at the company.

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NewsCred has a new – and semantic – take on content syndication, with content partners ranging from Reuters to The Guardian to The Economist. Recently-added customers that leverage the service’s fully licensed text, image and video content include traditional publishers such as the New York Daily News (and NewsCred is in talks with it about becoming a content provider, too). But other recent customers point to the importance of quality content to the consumer and corporate brand market:  For example, insurance provider Zurich recently signed on. NewsCred also just closed a deal with Johnson & Johnson to be a subscriber of its syndication services for content related to the health care products and pharmaceuticals space.

Brands, says NewsCred CEO Shafqat Islam, are responding to consumers getting smarter and more demanding. “They have so much access to information that brands are starting to realize they can’t just sell products or services anymore,” he says. “They need more authentic, engaging conversations with their customers and the best way to build these authentic relationships is with highly-engaging, trusted, high-quality content.”

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For example, there’s net Sentiment, which Market Strategies says represents the ratio of positive to negative sentiments expressed about a brand based on automated natural language processing of the content of posts, comments and mentions. Another component, Positive Emotions, seems to flow from that measure, representing the number of content items that are identified as having the warm fuzzies about them, again based on automated coding of content.

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