What’s on the menu for semantic technology this week? Two vendors in the foodie field are offering up some new treats.

From Nara, whose neural networking technology is behind a service to help users better personalize and curate their restaurant dining experiences (see how in our story here), comes a new feature that should make picking a restaurant for a group dinner an easier affair. It combines users’ “digital DNA” – the sum of what it learns of what each one likes and doesn’t like regarding dining venues – to serve up restaurant choices that should appeal to the entire group across its range of preferences.

“It’s a really fun way to start getting [the service] into social,” says Nara founder and CEO Tom Copeman.

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Swipp, which in January launched its social intelligence platform and consumer social networking app (see our story here), today follows through on the plans it alluded to then of letting businesses leverage its technology for merging social and knowledge streams. Structured data is at the heart of the Swipp platform, with the Freebase entity graph providing reference knowledge and context for topics; its value propositions are that comments are tied to a specific, exact topic and that it creates a real-time Index for users’ social data sentiment scores for each topic that can be combined and sorted by geography, time, gender, and age.

The new Swipp Plus tool suite, the company says, draws on its social intelligence platform to prepare businesses – from consumer brands to content providers – to better connect with customers on today’s social web. Swipp Plus now enables them to leverage capabilities in its platform to add a Swipp widget to their web sites, blogs, maps, QR codes, and various online arenas around pieces of content, particular products, or concepts, and it also is working to build out mobile capabilities for direct feedback.

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Facebook is integrating into its platform Rovi Video’s descriptive information on millions of TV shows, movies, celebrities, sports events and more. The partnership should help users flesh out more details in their entertainment likes and status updates that tag their movie, TV, and other media experiences in their profiles, and aid app developers in leveraging a standardized set of entertainment data for new apps and user-engagement.

Rovi Corp.’s media metadata Video Data set boasts standardized, structured data on more than 4 million programs, including theatrical, DVD and Blu-ray releases.

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In December the Semantic Web Blog spoke with Wikidata project director Denny Vrandecic about progress on Phase 1 of the work to create a free knowledge base about the world that can be read and edited by humans and machines (see story here). At the time, Vrandecic explained that January would begin the roll-out of language-by-language editions – first up were Hungarian, Hebrew and Italian – on the Wikipedias.

Last week brought another language on board, as Wikidata Phase 1 went live on English Wikipedia, with Wikidata language links supplementing locally-hosted ones there too.  March 6 should see deployment to the Wikipedias that do not have language links.

In an important update, Phase 2 of the overall effort to centralize access to and management of structured data – which was in development as Phase 1 progressed – saw its first fruits for use on Wikidata.org (not yet on Wikipedia) earlier this month: Infoboxes.

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One of the exciting things about being a semantic technologist is the opportunity to be in on the ground floor of things as companies revamp, revise, and renew their infrastructures for the Web 3.0 world.

That’s the position that Keith DeWeese finds himself in. DeWeese recently moved from The Tribune Company, where he led efforts in applying semantic technology to the publisher’s content (see story here), to Ascend Learning, a company that provides technology-based education products with a focus on the healthcare sector.

There, as principal content architect he is again championing the power of semantic technology for online content. “What’s cool is that Ascend is in a state of redefining what it does, how it works, its whole platform,” DeWeese says. Ascend wants to be able to take people from the beginning stages of their career, when they’re learning the basics, and work with them throughout their life, so that as they progress in their careers and become more knowledgeable about their profession or specialization and work toward different exams, it’s got the tools to engage with them at that part of their lifecycle.

“It’s really great because there’s an openness and willingness to try different approaches to making content available to end users.”

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Structured data makes the Web go around. Search engines love it when webmasters mark up page content. Google’s rich snippets, for instance, leverages sites’ use of microdata (preferred format), or RDFa or microformats: It makes it possible to highlight in a few lines specific types of content in search results, to give users some insight about what’s on the page and its relationship to their queries – prep time for a recipe, for instance.

Plenty of web sites generated from structured data haven’t added HTML markup to their pages, though, so they aren’t getting the benefits that come with search engines understanding the information on those web pages.

Maybe that will change, now that Google has introduced Data Highlighter, an easy way to tell its search engine about the structured data behind their web pages. A video posted by Google product management director Jack Menzel gives the snapshot: “Data Highlighter is a point- and-click tool that allows any webmaster to show Google the patterns of structured data on their pages without modifying the pages themselves,” he says.

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SKOS, which stands for Simple Knowledge Organization System, is a W3C standard, based on other Semantic Web standards (RDF and OWL), that provides a way to represent controlled vocabularies, taxonomies and thesauri. Specifically, SKOS itself is an OWL ontology and it can be written out in any RDF syntax.

Before we dive into SKOS, what is the difference between Controlled Vocabulary, Taxonomy and Thesaurus?

A controlled vocabulary is a list of terms which a community or organization has agreed upon. For example: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday are the days of the week.

A taxonomy is a controlled vocabulary organized in a hierarchy. For example, we can have the terms Computer, Tablet and Laptop and the concepts Tablet and Laptop are subclasses of Computer because a Tablet and Laptop are types of Computers.

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Photo credit: Flickr/Erwiss, peace&love

It’s that spooky time of year again. With a happy Halloween to all, we present a selection of Halloween entertainment to dive into between answering the door for trick-or-treaters, or whenever you might like to have a little scarefest. They all come courtesy of searches done on some of the web’s semantically-enabled platforms.

Movies, from Jinni.com:

A search on Jinni, the semantic movie and TV “taste engine” that we first covered here, for “serial killer” theme, set in the 20^th century in small towns, brings up some classics in the list of 41 that’s displayed, as well as some you may have missed when originally shown in theatres. Some in the list:

Halloween (of course): The 1978 John Carpenter-directed classic that started Jamie Lee Curtis on her fright-girl career (long before the yogurt days).  As a line in the summary says, the film “turned the slasher movie into a viable, successful genre. Halloween has been copied, parodied and even turned into a franchise of its own, but the original is still considered the best of the bunch.”

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A couple of years back, The Semantic Web Blog visited with Vijay Bulusu to gain some insight into how pharma giant Pfizer Inc. was moving forward with semantic technology (see article here). At last week’s Semantic Technology and Business Conference in New York City, Bulusu, director, informatics and innovation at Pfizer, provided additional perspective on the issue – first, during the presentation on Using Linked Semantic Data in Biomedical Research and Pharmaceuticals (see coverage of that here), and then in a follow-up conversation.

A struggle for pharma companies, Bulusu notes, sits in driving standards for data that exists across system silos, so it is broadly applicable across groups. A transaction like creating a batch of materials, doing analytical testing on it and enabling clinical trial releases is the work of multiple groups of people in departments like R&D entering data across different systems.

The foundational layer needed to support data aggregation in a persistent graph semantic database and visualization with collaborative, semantic knowledge maps “is all about data already in transactional, silo’d systems,” Bulusu says. “We want to make sure that across those systems, key data is entered consistently for entities.” That means limiting them to selecting via a drop-down list from a vocabulary that is consistently managed and published from a single source to all these transaction systems, so the same entity is called by the same name as it traverses systems to support analytics and other requirements. That, he says, “is where we directly impact the day-to-day operational work of users.”

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Google has released the structured data testing tool, a new and renamed version of its rich snippet testing tool. According to a blog by Yong Zhu, on behalf of the rich snippets testing tool team, improvements include:

• How rich snippets are displayed in the testing tool to better match how they appear in search results;
• A new visual design to make it clearer what structured data it can extract from the page, and how that may be shown in search results;
• And the availability of the tool in languages other than English (French, Spanish, Arabic, for example) to help webmasters from around the world build structured-data-enabled websites.

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