Derrick Harris reports that as data continues to explode, business owners don’t need to rush back to school for a degree in Big Data analytics in order to capitalize on it. He writes, “Machine learning is high data science — a discipline focused on algorithms that automatically detect complex patterns hidden within datasets — and it’s fast becoming something that anyone leverages to sell more handbags, or solve a research problem, or build the next LinkedIn or Facebook… If you’re a small business or maybe even an individual, you need something easier. You need something in the cloud. And you need someone else to handle the hard parts.” Read more

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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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Michael Cooney reports that next month the Defense Advanced Research Projects Agency (DARPA) is set to detail “the union of advanced technologies from artificial intelligence, computational linguistics, machine learning, natural-language fields it hopes to bring together to build an automated system that will let analysts and others better grasp meanings from large volumes of text documents.”

DARPA stated, “Automated, deep natural-language understanding technology may hold a solution for more efficiently processing text information. Read more

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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What do big companies have that most emerging businesses don’t have to help them get value from Big Data? Well, to start with, there’s lots of money and a ton of technology resources.

Never fear. At the upcoming Semantic Tech & Business conference in Berlin, Christopher Testa, CTO of startup WhiteBox Inc., plans to give companies with considerably fewer resources than giants like Google and IBM insight into how to use Big Data as a small, lean startup. His guidance will draw from his own past experiences at Google training AdSense; lessons learned studying the development of IBM’s Watson; and his current efforts to apply Big Data principles to create an expert system for amateur radio operator license exams at his own startup, with limited engineering resources. Most recently Testa was head of engineering at Ad.ly, and that will factor into advice about how to run a data center with free and open source solutions, too.

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Editor’s Note: Here at the Semantic Web Blog we’ve done a lot of coverage of the personalized news mag app space. That includes some in-depth looks into Zite, acquired by CNN in August, such as this article. Most recently, we brought you news of Zite’s iPhone app.

Today, over at Zite’s blog, the company today will run a piece entitled Zite: Under the Hood. It should be of interest to anyone who wants more details about how its technology operates. It goes like this:

Zite: Under the Hood

If you’re already a Zite user, you’ve experienced the delivery of personalized content that is updated every time you open the app. To make that transparent and easy for you, takes a lot of effort. The Zite team brings together decades of software development in artificial intelligence, machine learning and natural language technologies, and more than six years of product development, to blend and tune the experience for you. In short, Zite works by:

• mining content from your social web
• modeling that content
• modeling the community that interacts with it
• modeling your interests
• matching your interests to the content and your community, to help you discover content you’ll want to see.

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Tagged has acquired Topicmarks in the hopes of improving its friend suggestions. Josh Constine reports, “Tagged’s mission is to help strangers meet each other online, so it has to offer friend suggestions of people you’ll like and who’ll like you back. That’s why it acquired Topicmarks, a natural language processing and machine learning company. Topicmarks will allow Tagged to analyze the profiles of its 100 million registered users and match them with others with similar interests and vocabulary. Topicmarks’ technology, CEO, CTO, and 3 senior engineers will join Tagged in exchange for cash and stock. Its existing service will remain active for the foreseeable future.” Read more

Bill Ives recently discussed the advancements of Salience 5.0 with Lexalytics CEO Jeff Catlin. Ives writes, “Semantic technology differs from most computing as it learns on the job. This can provide great benefits but it can also be time consuming… [Lexalytics] came up with a clever idea to reduce the learning curve. They had their semantic engine digest Wikipedia to gain an understanding of human thought and build their Concept Matrices™. This allows it to do things that most computer technology would struggle with such as understanding that pizza is a food even though the word food was never associated with pizza in the text it was looking at.” Read more

A new article marvels at the advances of artificial intelligence, pointing to a news brief written by a computer: “WISCONSIN appears to be in the driver’s seat en route to a win, as it leads 51-10 after the third quarter. Wisconsin added to its lead when Russell Wilson found Jacob Pedersen for an eight-yard touchdown to make the score 44-3.” The article notes, “Those words began a news brief written within 60 seconds of the end of the third quarter of the Wisconsin-UNLV football game earlier this month… The clever code is the handiwork of Narrative Science, a start-up in Evanston, Ill., that offers proof of the progress of artificial intelligence — the ability of computers to mimic human reasoning.” Read more

Pearson has created a semantically powered tool to help students assess their reading abilities. The article explains that the Pearson Reading Maturity Metric is “a new and more accurate measure of the reading difficulty of texts. Developed by scientists at Pearson’s Knowledge Technologies group, the new computer-based technology measures how close an individual student’s reading abilities are to what they will need to succeed in college and careers.” Read more