Amit Chowdhry of Forbes reports, “Facebook has over a trillion status updates, text posts, photos and pieces of content archived, which is why the social network company has been heavily focused on improving its search engine. Over the last few years, Facebook displayed results from Bing.com for keyword searches since they had a partnership with Microsoft. However, Facebook recently decided to completely remove Microsoft Bing search results. ‘We’re not currently showing web search results in Facebook Search because we’re focused on helping people find what’s been shared with them on Facebook,’ said a Facebook spokesperson in an interview with Reuters. ‘We continue to have a great partnership with Microsoft in lots of different areas’.” Read more
Posts Tagged ‘knowledge graph’
SAN MATEO, CA, Dec 16, 2014 (Marketwired via COMTEX) — Neo Technology, creators of Neo4j, the world’s leading graph database, today announced that Pitney Bowes Inc. a provider of technology solutions for small, mid-size and large firms that help them connect with customers to build loyalty and grow revenue, is successfully using Neo4j to build and maintain a comprehensive knowledge graph of critical information across data and application silos as part of its Spectrum Master Data Management (MDM) offering. Read more
AlchemyAPI’s New Face Detection And Recognition API Boosts Entity Information Courtesy Of Its Knowledge Graph
AlchemyAPI has released its AlchemyVision Face Detection/Recognition API, which, in response to an image file or URI, returns the position, age, gender, and, in the case of celebrities, the identities of the people in the photo and connections to their web sites, DBpedia links and more.
According to founder and CEO Elliot Turner, it’s taking a different direction than Google and Baidu with its visual recognition technology. Those two vendors, he says in an email response to questions from The Semantic Web Blog, “use their visual recognition technology internally for their own competitive advantage. We are democratizing these technologies by providing them as an API and sharing them with the world’s software developers.”
The business case for those developers to leverage the Face Detection/Recognition API include that companies can use facial recognition for demographic profiling purposes, allowing them to understand age and gender characteristics of their audience based on profile images and sharing activity, Turner says.
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
- 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.
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