Semantic technology is scoring more goals in the sports world. The BBC, for example, which created the FIFA World Cup 2010 website that leveraged semantic technology, is at it again as London prepares for the 2012 Summer Olympics. Brazil has gotten into the action, too, with an Internet portal there taking soccer to the semantic web set. At the upcoming SemTech conference in San Francisco, attendees will have an opportunity to hear the latest details about both efforts.

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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Edamam wants to be the one place where all the food knowledge of the world is organized. That’s the goal of co-founder and CEO Victor Penev, who launched the site in April, and recently updated the several hundred major recipe sites in its knowledge base to also include some smaller blog sites that add additional variety.

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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Konstantin Kakaes of the New America Foundation recently discussed the NLP challenges of language translation. Kakaes writes, “Recently, on the eighth floor of an office building in Arlington, Va., Rachel held her finger down on a Dell Streak touchscreen and asked Aziz whether he knew the village elder. The handheld tablet beeped as if imitating R2-D2 and then said what sounded like, ‘Aya tai ahili che dev kali musha.’ Aziz replied in Pashto, and the Streak said in a monotone: ‘Yes, I know.’ Rachel asked: ‘Would you introduce me to him?’ Aziz failed to understand the machine’s translation (though he does speak English), so she asked again: ‘Could you introduce me to the village elder?’ This time, there was success, after a fashion. Aziz, via the device, replied: ‘Yes, I can introduce myself to you.’ Aziz, who is at most middle-aged and was wearing a sweater vest, was not the village elder.” Read more

The financial services sector was in focus at this week’s Sentiment Analysis Symposium in New York City, which is organized and produced by Alta Plana Corp. and its founder, Seth Grimes.  Take, for example, the presentation by Rich Brown, head of Elektron Analytics at Thomson Reuters, who disclosed that the company is about to launch market response indicators in support of its Thomson Reuters News Analytics system for the financial community. That product this week also won The Technical Analyst’s 2012 award for best news analytics software.

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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Wavii made waves when it launched  its automated, algorithm-driven news aggregation service in April, which has been billed as making Facebook out of Google. But what makes Wavii work? When The Semantic Web Blog found a picture in Wavii.com’s Flickr photostream featuring the word “predicates” on a white board, it was time to discover whether Semantic Web standards had anything to do with its engine.

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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While much of the publishing industry still is getting up to speed on what semantic technology can do for business, it’s already deep within the DNA of The Tribune Company – to the point where Keith DeWeese, Director, Information and Semantics Management, can comfortably use the word “ontology” in discussions with non-tech employees, and enjoy the fact that they’re equally comfortable using it themselves.

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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These days, it’s not just the traditional publishing community that has reason for leveraging the content syndication model. As more and more companies across vertical sectors themselves become content providers, syndication makes sense for them, too.

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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Market Strategies International recently released the first edition of what it says will be an annual Social Media Brand Index, a measure for brands both of consumer-generated social media about them and of their own sponsored content. The Index takes into account four components. Volume, or the amount of buzz about a brand online, is one of them — and its most highly weighted component, too. The others take their cue from what we might call more meaning-related measures, sentiment analytics and semantic markup among them.

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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Just a little over a year ago The Semantic Web Blog introduced our readers to Gravity in this article. The project, spearheaded by former MySpace execs, is focused on building the Interest Graph. The team’s been pretty quiet about development efforts since that time — until just this month, when it announced Gravity Labs to let the public in on a little more about its underlying Interest Graph infrastructure and to showcase the platform. It also announced that it was open-sourcing some of the “plumbing” code it came up with during development, while understandably keeping its core IT, ontology and algorithms under wraps.

The announcement noted that the internally-named Gravity Interest Service for personalizing content at scale, in real-time, went live at production-scale 6 months ago. So far the technology has created over 400 million user interest graphs; served over 13 million pieces of personalized content per day; personalized the daily Internet experience of tens of millions of users per month; and processed over 25 million inbound interest signals per day, the company says. It expects that at this rate, that in under six months it will be handling 10X all of these numbers.

The Semantic Web Blog once again caught up with Gravity CTO Jim Benedetto to talk some more about the Interest Graph, a term he acknowledges gets thrown around quite a bit these days, with a lot of web sites claiming they’ve got the goods. But, he says, “what they effectively are saying is that buried deep within the data of our logs or deep in the data of how our users interact with our site, we know there are interest indicators there. But a lot of them are not doing much with their data.” Interest Graphs, he says, aren’t owned, but interest data resides in individual places and across the web at large — and they need the Gravity platform to help unlock that to create dynamic and personalized experiences for users, Benedetto says.

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Watson’s gone into banking: Citi is evaluating ways that IBM’s Deep QA technology can help advance digital banking efforts, through analysis of customer needs and processing tons of up-to-the-minute financial, economic, product and client data, according to a press release.

“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.