How Semantic and Social Search Are Evolving: Lessons From the Evri-Twine Merger

the media partnership aspect of its business; that includes deals with organizations like Hearst, The Washington Post, and The Times of London.

But the company’s central vision, he says, is to deliver “intelligent [data] streams to consumers” that, in effect, reduce their need to do traditional Web searches. That means providing them with a steady stream of personalized news and information gleaned from the Web. Instead of tracking five or six categories on Google News, you could create thousands of categories and receive feeds about any topic, however specific—in sports, you might get streams about major league baseball pitchers, football players with Super Bowl wins, or college basketball teams in the NCAA tournament. It could be like an automated, smarter version of Twitter—or it could actually incorporate Twitter, helping you sort tweets by meaning or category.

Evri says its intellectual property position is particularly strong after gaining some key patents from Radar Networks, including ones on natural language processing and semantic understanding of text. (Twine was less focused on real-time streams, and more on learning about people’s interests.) The merged company’s patent portfolio now includes more than 15 issued patents and 40-some others pending review. Hunsinger says the merger “makes us the lead dog in the semantic search and discovery space.” He adds, “Now we have to figure out how one plus one equals three.”

Indeed, it will be interesting to see how Evri makes use of the hard lessons Radar Networks learned over the past couple of years. Many of these lessons were detailed in an extensive blog post by Radar Networks’ Spivack last week. They include nuggets like his advice to early-stage entrepreneurs about raising less money from VCs, spending less, getting to profitability quickly, and not staying on as the long-term CEO. All in all, it doesn’t sound very encouraging, and it’s enough to make anyone wonder whether semantic search is still a solution looking for a problem. But perhaps handling social, real-time search in a better way could be that problem.

“This will certainly splash some cold water on investors,” says Steve Judkins, founder of Seattle-based MemeSpring, an early-stage startup focused on semantic search. Still, he remains

Author: Gregory T. Huang

Greg is a veteran journalist who has covered a wide range of science, technology, and business. As former editor in chief, he overaw daily news, features, and events across Xconomy's national network. Before joining Xconomy, he was a features editor at New Scientist magazine, where he edited and wrote articles on physics, technology, and neuroscience. Previously he was senior writer at Technology Review, where he reported on emerging technologies, R&D, and advances in computing, robotics, and applied physics. His writing has also appeared in Wired, Nature, and The Atlantic Monthly’s website. He was named a New York Times professional fellow in 2003. Greg is the co-author of Guanxi (Simon & Schuster, 2006), about Microsoft in China and the global competition for talent and technology. Before becoming a journalist, he did research at MIT’s Artificial Intelligence Lab. He has published 20 papers in scientific journals and conferences and spoken on innovation at Adobe, Amazon, eBay, Google, HP, Microsoft, Yahoo, and other organizations. He has a Master’s and Ph.D. in electrical engineering and computer science from MIT, and a B.S. in electrical engineering from the University of Illinois, Urbana-Champaign.