Josh Ong of The Next Web reports, “Google today revealed details behind a new search feature called Structured Snippets that displays information pulled from data tables on webpages. The feature actually began rolling out last month, but the company’s research team explained the technology in a post today. The search engine has been progressively adding new information through its Knowledge Graph database. This latest feature adds more data below the snippets of text in a search query.” Read more
Posts Tagged ‘knowledge graph’
- In a sample of over 12 billion web pages, 21 percent, or 2.5 billion pages, use it to mark up HTML pages, to the tune of more than 15 billion entities and more than 65 billion triples;
- In that same sample, this works out to six entities and 26 facts per page with schema.org;
- Just about every major site in every major category, from news to e-commerce (with the exception of Amazon.com), uses it;
- Its ontology counts some 800 properties and 600 classes.
A lot of it has to do with the focus its proponents have had since the beginning on making it very easy for webmasters and developers to adopt and leverage the collection of shared vocabularies for page markup. At this August’s 10th annual Semantic Technology & Business conference in San Jose, Google Fellow Ramanathan V. Guha, one of the founders of schema.org, shared the progress of the initiative to develop one vocabulary that would be understood by all search engines and how it got to where it is today.
Barbara Starr of Search Engine Land recently observed that, “Search is changing – and it’s changing faster than ever. Increasingly, we are seeing organic elements in search results being displaced by displays coming from the Knowledge Graph. Yet the shift from search over documents (e.g. web pages) to search over data (e.g. Knowledge Graph) is still in its infancy. Remember Google’s mission statement: Google’s mission is to organize the world’s information to make it universally accessible and useful. The Knowledge Graph was built to help with that mission. It contains information about entities and their relationships to one another – meaning that Google is increasingly able to recognize a search query as a distinct entity rather than just a string of keywords. As we shift further away from keyword-based search and more towards entity-based search, internal data quality is becoming more imperative.”
In Part 3 of this series, Jarek Wilkiewicz details activating the small Knowledge Graph (built on Cayley) with Schema.org Actions. He begins by explaining how Actions can be thought of as a combination of “Entities” (things) and “Affordances” (uses). As he defines it, “An affordance is a quality of an object, or an environment, which allows an individual to perform an action.”
For example, an action, might be using the “ok Google” voice command on a mobile device. The even more specific example that Wilkiewicz gives in the video (spoiler alert) is that of using the schema.org concept of potentialAction to trigger the playing of a specific artist’s music in a small music store’s mobile app.
To learn more, and to meet Jarek Wilkiewicz and his Google colleague, Shawn Simister, in person, register for the Semantic Technology & Business Conference where they will present “When 2 Billion Freebase Facts is Not Enough.”
Barak Michener, Software Engineer, Knowledge NYC has posted on the Google Open Source Blog about “Cayley, an open source graph database.”: “Four years ago this July, Google acquired Metaweb, bringing Freebase and linked open data to Google. It’s been astounding to watch the growth of the Knowledge Graph and how it has improved Google search to delight users every day. When I moved to New York last year, I saw just how far the concepts of Freebase and its data had spread through Google’s worldwide offices. I began to wonder how the concepts would advance if developers everywhere could work with similar tools. However, there wasn’t a graph available that was fast, free, and easy to get started working with. With the Freebase data already public and universally accessible, it was time to make it useful, and that meant writing some code as a side project.”
The post continues: “Cayley is a spiritual successor to graphd; it shares a similar query strategy for speed. While not an exact replica of its predecessor, it brings its own features to the table:RESTful API, multiple (modular) backend stores such as LevelDB and MongoDB, multiple (modular) query languages, easy to get started, simple to build on top of as a library, and of course open source. Cayley is written in Go, which was a natural choice. As a backend service that depends upon speed and concurrent access, Go seemed like a good fit.”
Straight out of Google I/O this week, came some interesting announcements related to Semantic Web technologies and Linked Data. Included in the mix was a cool instructional video series about how to “Build a Small Knowledge Graph.” Part 1 was presented by Jarek Wilkiewicz, Knowledge Developer Advocate at Google (and SemTechBiz speaker).
Wilkiewicz fits a lot into the seven-and-a-half minute piece, in which he presents a (sadly) hypothetical example of an online music store that he creates with his Google colleague Shawn Simister. During the example, he demonstrates the power and ease of leveraging multiple technologies, including the schema.org vocabulary (particularly the recently announced ‘Actions‘), the JSON-LD syntax for expressing the machine readable data, and the newly launched Cayley, an open source graph database (more on this in the next post in this series).
Standard Analytics, which was a participant at the recent TechStars event in New York City, has a big goal on its mind: To organize the world’s scientific information by building a complete scientific knowledge graph.
The company’s co-founders, Tiffany Bogich and Sebastien Ballesteros,came to the conclusion that someone had to take on the job as a result of their own experience as researchers. A problem they faced, says Bogich, was being able to access all the information behind published results, as well as search and discover across papers. “Our thesis is that if you can expose the moving parts – the data, code, media – and make science more discoverable, you can really advance and accelerate research,” she says.
A couple of weeks back The Semantic Web Blog reported on research from SEO optimization vendor Searchmetrics about the virtues of semantic markup. Now the 2014 Content Search Marketers Survey, which recently came out from enterprise SEO platform vendor Brightedge, adds some more interesting statistics to show about what matters to optimized search.
Among them: Half of the respondents consider a page/content-based approach to driving page traffic, conversions and revenue as being much more important for SEO in 2014 than in 2013. Another 50 percent said it would be more or as important this year than last.
“The page-based approach to SEO in the world of secure search is important for 100 percent of SEOs, and 85 percent stated that it would be more or much more important for them in 2014,” the report states. “SEOs are also still focused on the business impact of the keyword (90 percent), though the shift in focus to the page leaves only 50% percent stating that measuring the business impact of the keyword will be more important in 2014.”
MindMeld – you may know the term best from StarTrek and those fun-loving Vulcan practices. But it lives too at Expect Labs, as an app that listens to and understands conversations and finds relevant information within them, and as an API that lets developers create apps that leverage contextually-driven search and discovery – and may even find the information users need before they explicitly look for it.
Anticipatory computing is the term Expect Labs uses for that. “This is truly a shift in the way that search occurs,” says director of research Marsal Gavaldà. “Anticipatory computing is the most general term in the sense that we have so much information about what users are doing online that we can create accurate models to predict what a user might need based on long-ranging history of that user profile, but also about the context.”
The more specific set of functionality that contributes to the overarching theme of anticipatory computing, he explains, “means that you can create intelligent assistants that have contextual search capabilities, because our API makes it very easy to provide a very continuous stream of updates about what a user is doing or where a user is.”
The funding, which amounts to $1,8M will expand SpazioDati’s technology team and improve the capacity of its Knowledge Graph APIs and Semantic Text analysis APIs––both technologies available on SpazioDati’s data marketplace, dandelion.eu Read more
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