Seth Grimes recently set the record straight regarding the terms “text analytics” and “semantic content enrichment.” Grimes starts with a few definitions of text analytics: “Text analytics is a set of software and transformational steps that discover business value in ‘unstructured’ text. (Analytics in general is a process, not just algorithms and software.) The aim is to improve automated text processing, whether for search, classification, data and opinion extraction, business intelligence or other purposes.” He adds, “Text analytics draws on data mining and visualization and also on natural-language processing (NLP). Supplement NLP with technologies that recognize patterns and extract information from images, audio, video and composites and you have content analytics.” Read more

Semantic Tech & Business Conference Returns to San Francisco

Semantic Tech & Business Conference returns to San Francisco in June! Join us from June 3-7 for complete coverage of Big Data, Linked Data, Extreme Information Management, and Semantic Web. From breakthrough approaches to solving business problems to the big data implications of fast–evolving technologies, SemTechBiz provides you with an unparalleled interactive experience and delivers tangible business value. We're offering a special early rate when you register by February 17. Sign up now!

Zemanta’s inked a deal with Federated Media that could make brand advertising via blogosphere contributions scale higher, faster. Federated Media lets independent bloggers participate as partner-publishers in programs to build brand engagement. For example, for Dockers, Federated Media had publishers with a strong male following write articles around the theme of men crafting lifetime legacies, which were used to increase page-views and clicks to, and reader interaction with, Dockers.

Zemanta’s technology has its roots in helping bloggers with suggestions of tags, links, photos, related articles, and more to add contextual relevance to their content. It analyzes posts using proprietary natural language processing and semantic algorithms, and statistically comparing its contextual framework to its pre-indexed database of content to supply its recommendations. Zemanta content sources include Wikipedia, IMDb, Amazon, Flickr, Crunchbase, Rotten Tomatoes and Freebase, among many others.

Zemanta CTO Andraz Tori says that the big advantage Zemanta brings to the relationship is that it has lots of engaged bloggers as well as the technologies to ‘understand’ what they are writing about. “FM usually worked just with couple of hundred bloggers and we’ll be able to scale that to many more,” he says.

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iXiGO, a travel meta search engine has launched a new flight search tool for Facebook and Twitter that utilizes natural language processing. According to an article by Anupam Saxena, “Users can directly post a query on iXiGO’s Facebook page or make a twitter mention to @iXiGOsearch with the query. iXiGO says that it will reply to the user with a comment on his query with details of the cheapest flight found across multiple travel sites, a link to the relevant iXiGO.com result page where the user can filter the results by time, stops, price and other factors, as well as a direct booking link for booking the cheapest flight on the travel site where it is available.” Read more

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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Out with the old, in with the new. We’ve covered (here and here) the year past for the semantic web. So now let’s see what might be in store for the year ahead.

Also, don’t forget to listen to our podcast here for more insights into what 2012 may hold.

• Interest in sentiment analysis exploded with the growth of the social Web, although its reputation suffered due to the prevalence of low-grade Twitter-sentiment toys, simplistic, wildly inaccurate systems that misled many into criticism of the concept where it was the cheap implementations they’d tried that were faulty.  In 2012, sentiment analysis will come into its own: Automated (and crowd-sourced!) mining of attitudes, opinions, emotions, and intent from social and enterprise sources, at the “feature” level, linked to real-world profiles and transactional data. — Seth Grimes, founder, Alta Plana Corp

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To accompany our recent podcast looking back on 2011, we’ve accumulated some additional perspectives from thought leaders in the next-wave Web space on the year that’s quickly passing us by.

Some highlights follow. You’ll see respondents hit on some common themes throughout, such as Big Data, sentiment analytics, specific vertical industry adoption, and the standards space:

 

• SKOS has become an increasingly popular entry point for organizations that want to use semantic technology in practical applications without worrying about the more complicated aspects of semantic web technology. – Bob  DuCharme, solutions architect, TopQuadrant

 

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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

A new article reports, “CodeBaby, a company whose award-winning digital characters are shaping the growing online self-service marketplace, today announced its partnership with inbenta, a leader in Natural Language Processing and Semantic Search. Recognizing the expanding market demand for self-service solutions, CodeBaby and inbenta have joined forces to deploy intelligent virtual assistants that increase online conversation rates and customer satisfaction.” Read more

Stanford is offering another free online course: this time, the subject is Natural Language Processing taught by Chris Manning and Dan Jurafsky. According to the description, “The course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.” Read more