“With more features in the Knowledge Graph and more languages, with conversational voice search and hot-wording coming to Chrome on desktops and laptops, and with new Now functionality like reminders….search is becoming a really beautiful and ubiquitous experience that intelligently answers your questions and assists you throughout the day across all screens.”

That’s how Google Fellow Amit Singhal summed up the evolving search experience at today’s Google I/O event. Here’s more about the latest features:

• Google’s Knowledge Graph, now some 570 million entities strong and growing, is taking it to the stats. Now, users will get important stats powered by the Knolwedge Graph, he said. “Already you can find answers to questions like what is the population of India,” he told the audience, “but starting today we will anticipate your next question,” which may be how that population compares to the population of other countries. So, you’ll get the answer alongside the trends line and see all that in comparison to the population of the two countries whose population is most often compared to India, China and the U.S. Google Knoweldge Graph is also boosting its language support, adding to the existing eight Polish, Turkish, simplified and traditional Chinese.
• Users in the Gmail search trial already have the capability of finding answers – like when is their upcoming flight or restaurant reservation — without having to sift through email, docs and calendar data. But, said Singhal, things can get better when it comes to letting users get those answers in the most natural way possible, which means Google has been working hard on technologies like voice recognition and natural language understanding. To that end, conversational search, already available on Android and iOS, is coming to all desktops and laptops through Chrome, he said.
• Joining conversation search is hot-wording, a new interface, or, as he calls it, a “no interface,” where users can ask their search questions without clicking on the mike. Just preface a voice question with, “OK Google,” and Google will speak back the answer to you, drawing among other sources on its Knowledge Graph for the response. Google product manager Johanna Wright gave a demo of the voice experience courtesy of Chrome on a mobile device, working her way through planning a day trip to Santa Cruz through to images of its beach boardwalk, asking “OK Google, how far from here to it?,” where Google, in speaking back the answer, recognized that it referred to the boardwalk and that here was her current location.
• Enter Google Now: Singhal talked up anticipation (it’s more fun if you pronounce it like Tim Curry in the Rocky Horror Picture Show number), and the usefulness of having the right answer suggested at the right time, even before a user asks. That’s what is set to happen with an on-the-way feature that lets users set reminders in Google Now to show up when they need them. Also launching on the Google Now front are other new cards: public transit commute time cards and more cards for music albums, tv shows, and video games. Google is now “even more useful as an assisted tool,” he said.

Of the new age of search, Singhal said it’s not around the corner, that it will be some time before this becomes the predominant search experience. “There are lots of complex and scientific problems to solve, but our investment and commitment to getting there sooner rather than later is immense.”

Photo courtesy: Flickr/rvaphotodude

Last month at its MarkLogic World 2013 conference, the enterprise NoSQL database platform provider talked semantics as it related to its MarkLogic Server technology that ingests, manages and searches structured, semi-structured, and unstructured data (see our story here). The vendor late last week was scheduled to provide an early access release of MarkLogic 7, formally due by year’s end, to some dozens of initial users.

“People see a convergence of search and semantics,” Stephen Buxton, Director, Product Management, recently told The Semantic Web Blog. To that end, a lot of the vendor’s customers have deployed MarkLogic technology as well as specialized triple stores, but what they really want, he says, is an integrated approach, “a single database that does both individually and both together,” he says. “We see the future of search as semantics and the future of semantics as search, and they are very much converging.” At its recent conference, Buxton says the company demonstrated a MarkLogic app it built to function like Google’s Knowledge Graph to provide an idea of the kinds of things the enterprise might do with both search and semantics together.

Following up on the comments made by MarkLogic CEO Gary Bloom at his keynote address at the conference, Buxton explained that, “the function in MarkLogic we are working on in engineering is a way to store and manage triples in the MarkLogic database natively, right alongside structured and unstructured information – a specialized triples index so queries are very fast, and so you can do SPARQL queries in MarkLogic. So, with MarkLogic 7 we will have a world-class triple store and world-beating information store – no one else does documents, values and triples in combination the way MarkLogic 7 will.”

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One in 50 American children have autism, according to the latest figures released by the Centers for Disease Control and Prevention in March. One of the winners of the YarcData Graph Analytics Challenge, announced in April, can make a difference in better understanding the causes of the disease.

Taking second place in the competition, the work of Adam Lugowski, Dr. John Gilbert, and Kevin Dewesse, of the University of California at Santa Barbara, leveraged a dataset created for the Mayo Clinic Smackdown project, that has the same structure and property types – and scale – as the medical organization’s actual Big Data sets around autism, but which uses publicly available data in place of the real thing. The team can’t use the real data because it includes private information about patients, diagnosis, prescriptions, and the like.

But the actual data deployed for the project doesn’t matter, says Lugowski . “The goal is to find relationships we have never thought of before, and this way it doesn’t prejudice the algorithm,” he says. Using YarcData’s uRIKA graph analytics appliance, the algorithm queries the Smackdown dataset – which in its smallest version has almost 40 million RDF triples and in its largest is about 100 times bigger, mirroring the size of all the Mayo Clinic’s actual autism data – to discover commonalities among the data, mimicking how the real data sets could be queried in search of common precursors among clusters of patients with the diagnosis.

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Courtesy: Flickr/ Sean MacEntee

Earlier this month word came of a revision to schema.org: Version 1.0a additions, according to this posting from Dan Brickley, include the Datasets vocabulary, and some supporting utility terms for describing schema.org types, properties and their inter-relationships. One of the gems in the update are additions related to the Learning Resource Metadata Initiative (LRMI), an effort led by the Association of Educational Publishers and Creative Commons, which has as its goals making it easier to publish, discover and delivery quality educational resources on the web. The Bill and Melinda Gates Foundation and the William and Flora Hewlett Foundation helped fund the work.

With schema.org serving as a catalyst for its work, the LRMI developed a common metadata framework for tagging online learning resources, with the idea of having that metadata schema incorporated into Schema.org. With that now the case, it’s possible for publishers or curators of educational content to use LRMI markup and have that metadata recognized by the major search engines.

“One of the reasons why education was one of the first extensions of schema.org is that the education industry is going through some very interesting times,” says Madi Weland Solomon, head of Data Architecture Standards at education company Pearson plc, one of the LRMI project launch partners.

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Image Courtesy: Flickr/Patrick Hoesly

The financial services industry is taking to semantic tech in an important way, and that’s in the form of the Financial Industry Business Ontology (FIBO), which aims to standardize the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions. Attendees at SemTech Biz in San Francisco will get a deep dive on the how’s and why’s, at this session, while the FIBO Technology Summit invitation event will present an opportunity for working collaboratively to continue advancing the effort that has its roots in The Enterprise Data Management Council and communities of interests.

Leading that event will be Dennis E. Wisnosky, founder of Wizdom Systems, Inc. and former CTO and Chief Architect of the DoD Business Mission Area, who was recently named to provide technical strategy and operational guidance to help the Council finalize and implement FIBO standards, and David S. Newman, SVP & Strategic Planning Manager Enterprise Architecture at Wells Fargo, and Chair of the EDM Council’s Semantics Program. (Newman, with Enterprise Data Management Council Head of Semantics and Standards Mike Bennett, will also host the SemTech FIBO session.)  Speaking of the upcoming event, Wisnosky explains that a goal is to cast a wide net to find the new tech ideas and developments that both can bring benefits to FIBO in the short term and influence the longer-term research agenda to help the financial industry.

As FIBO stands now, in June the second draft of the FIBO Foundations ontology and the conceptual FIBO Business Entities ontology will be presented at a meeting of the Object Management Group in Berlin. By year’s end it is expected that the OMG will have ratified these as formal standards. “We are on the path to turn the corner from thinking of what FIBO will be to delivering it,” says Wisnosky. Read more

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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The Research Data Alliance (RDA) was recently formed with the goal of accelerating international data-driven innovation and discovery. It aims to get there by facilitating the sharing and exchange of research data, its use and re-use, standards harmonization, and discoverability.

Funded by The Australian Commonwealth Government through the Australian National Data Service, the European Commission through the iCordi project under the 7th Framework Program, and the U.S. through the RDA/US activity supported by the National Science Foundation, it began its work last August when it established an international steering group with these funds. In March, it held its first plenary meeting and had its official launch.

Dr. Francine Berman, Professor of Computer Science, Rensselaer Polytechnic Institute, is Chair of the Research Data Alliance/U.S. The Semantic Web Blog recently conducted a  Q&A with her to learn more about RDA plans:

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The cloud’s role in processing big semantic data sets was recently highlighted in early April when DERI and Fujitsu Laboratories announced a new data storage technology for storing and querying Linked Open Data that resides on a cloud-based platform (see our story here).

The cloud conversation, with storage as one key discussion point, will continue to be an active one in Big Data circles, whether users are working with massive, connected Linked Data sets or trying to run NLP across the Twitter firehose. CloudSigma, for example, recently publicly disclosed that it is using an all solid-state drive (SSD) solution for its public cloud offering that lets users purchase CPU, RAM, storage and bandwidth independently. The use of SSD, says CEO Robert Jenkins, avoids the problem that spinning disks have with the randomized, multi-tenant access of a public cloud that leads to storage bottlenecks and curbs performance.

That, combined with the company’s approach of letting customers size virtual machine resources as they like, as well as leverage exposed advanced hypervisor settings to optimize for their particular applications, he says, brings the use of the public cloud infrastructure closer to what companies can get out of private cloud environments, and at a price-performance win.

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