Evri Blog

We’ve gotten some great feedback from our early beta users, and we’ve been busy at Evri world HQ working on updates to Evri.com, the Evri widgets, and the rest of our products. Probably the single most frequent feedback request was: “I am looking for a specific person (or place or thing) — where’s the “Find” box?” Well, we heard you, and it’s now there — a brand-spanking new Find box on every page. Just start typing, and if we have a match, it’ll show up in the drop down list. Pick what you are interested in, and you will go right to the Evri profile page for that topic.

What else is new? Here are some highlights:

More entities - We have doubled the number of People, Products, and Things for which we have profile pages. As you can see from the Find list image above, we even have Klingons!

Navigation and UI Improvements - we’ve made some changes to the user interface and experience to improve how the site and widgets work. The home page lists are now streamlined, and we have made it easier to get right to a profile page (just click on the Evri icon). On the profile pages, we have grouped things together in what we think is a more logical fashion, and added better navigation to related profile pages.

New widget types, and a special page for them! Content publishers, bloggers, and anyone interested in cool new widgets should look at our new Partners page. We have some new widget types on display to show how Evri can increase user engagement on your site. If you are a blogger or other publisher that is interested in Evri, please contact us.

Also, lots of improvements to performance, stability, and overall quality (at least, we think so.) We would of course love to know what you think as well — if you haven’t signed up for the beta, please do. And, if you already have, please send us feedback on the new features.

Here at Evri, we talk a lot about searching less. When we talk about searching less, we are talking about you, our users with precious time — we want you to search less — we aren’t talking about our machines, because they do an awful lot of searching so you don’t have to. So how are they, our racks and racks of computers, searching so you can understand more?

Well it comes down to teaching our machines to read documents more similar to the way humans do - to basically understand more of the meaning of the documents they index. This is very different from what traditional keyword based search technology does. Typical search engines, when they encounter a document, treat the document like a bag of words — associations between the words, how they interconnect, and form actual meaning is lost. Consider the following text snippet from a Starpulse article:

Howard insists they won’t be copying Radiohead’s idea and making their disc only available on the internet. [...] He tells BBC Radio 1, “We won’t be doing the same thing as Radiohead, no.” [...] Last year, Radiohead released In Rainbows as an Internet download and allowed fans to name their own price for the album.

Now from this snippet of text, your favorite search engine will store this data something like:

Radiohead - 3
Howard - 1
Rainbows - 1
released - 1
Internet - 1

and so on. I’m simplifying things a lot for the sake of discussion, but basically, your favorite search engine is maintaining a list of words, and keeping track of how many times those words appear in a given document. This approach works quite well for finding websites, but not very well for discovering facts, or relationships describing how people, places and things interconnect.

Now consider how Evri’s approach is different. For this same snippet of text, our machines will break the snippet out into multiple sentences. For each sentence, our machines will, in essence, diagram the sentence similar to what you did back in 7th grade grammar class. So, for every grammatical clause in a sentence, our system creates a data structure like that shown below.

In addition, our system knows that Radiohead is a band, released is a verb, and In Rainbows is an album. If a sentence said: Radiohead of Oxfordshire may release an album called In Rainbows, our system will store Oxfordshire as the suffix modifer of Radiohead, and will store the verb release as being conditional; knowing that a verb is conditional or negated is important as this information can be used to determine where in a list of results this relationship should appear. In addition, if a subsequent sentence says something like: The band’s experiment proved successful., our system will know that The band refers to Radiohead; this is because our system attempts to resolve anaphora similar to the way humans do. Finally, this triplet style data structure is searchable at web scale and web speed by searches expressible in a query language; this query language is quite flexible, but basically allows our recommendation and information navigation applications to formulate effective queries in a precise manner. For example, a query like:

is being used to render the right column in the entity detail page shown in the screen shot below.

When you actually click on a person or organization, like Billy Corgan, the system will execute a more refined query like:

One of the challenges our scientists and engineers face is how to formulate these types of queries in clever ways so you, the user, do not have to; I’ll save this discussion for another day, however.

Finally, we published a book chapter last year that does a more thorough job explaining our approach, and additional grammatical treatments our system performs. So if you’re interested, see the Natural Language Processing and Text Mining book chapter titled A Case Study in Natural Language Based Web Search.

The quote in the title is from an (in)famous 1980 interview with John Lydon (Rotten) and Keith Levene of the group Public Image Limited. They spent a good portion of the time saying, “We’re not a band, we’re a company” to an obviously perplexed host, Tom Snyder.

Other than appealing to post-punk fanboys, why I am talking about this? Well, the PiL boys are raising a point near and dear to our hearts — what is the difference between a “band” and a “company”? And, how do you tell the difference programmatically? This is important because we use software to ‘read” web content, extract all of the named entities and then try to categorize them against our standard ontology.  We refer to the individual things (Barack Obama, James Bond, and Paris, France) and subjects (Grammy Awards, World War II, and USA Patriot Act) of the world our users would want to know more about or understand the connections between as ‘entities.’

At Evri, we use six ontology types - People, Locations, Organizations, Products, Events, and Concepts (the last four are grouped as Things on our homepage.) These are intentionally broad — they are intended to be the most immutable part of our description of things — once a person, always a person. We have a couple of other ‘root’ level types - Temporal and Numeric - that are used, and are useful when analyzing content and making recommendations, but not shown directly in the User Interface. These are not fixed for all time — as the scope of our entity coverage grows we have built things in a way to make is easy to add more basic types.

For the more dynamic description of an entity’s characteristics or role, we use the term facet. Each entity can be of only one basic type, but can have many facets. A location is only a Location, but it can have multiple facets (State Capital and County Seat, for example.) The dynamic and extensible nature of facets let’s us rapidly respond to emerging descriptions in the web content our systems analyze. You can think of facets as a kind of tagging.

We show the current facet(s) of an entity at the top of each profile page. You can see this in the screenshot of the top of Bono’s Evri Profile, that his facet’s are ‘Musician’ and ‘Activist.’

We use facets in many ways. First, to provide information to the user — so that you know more about the person, place or thing you are looking at. But, it’s not just for display. We use this information to help with our document analysis systems. Facet information helps with entity disambiguation, for example. It’s very important for us to have the highest degree of precision identifying which particular ‘Michael Jackson” is referred to in content on the web. Unlike keyword search, being correct here is crucial to what we are building.

Facets also help with making recommendations. Our systems, and our curators, learn what kinds of activities and relationships occur most often for particular facets. We can then use this information to create templates to highlight these actions and relationships in our user interface. For example, ‘Musician’ facets are often “performing” so we make sure that we highlight this action, if it exists, on a musician’s profile page.

Lastly for now, I would mention that once we figure out our browse UI,  an important way to explore our Evri profiles will be by pivoting on these facets.

We will have future posts from the team that works on these systems as soon as they finish them

We have made it possible now for those of you in the Beta Preview to see how the first version of the Evri widget works. If you go to the main beta page you will find a list of three recent news headlines at the bottom of the page. Click on one of these to display the article, then click on the large banner in the middle of the page.

We have also made it a bit easier to get to Evri Profile Pages from the Home page and elsewhere. Based on your feedback, we realized that this was a bit opaque… We have some bigger changes coming at the end of this sprint, but for now, we have added a simple way to go directly to a Person, Place, or Thing’s Profile Page. Just look for the small Evri icon next to the name, and click! You get right to where you wanted to go.

We’ve gotten a lot of good, constructive coverage for our beta prieview. Thanks to all who wrote and who have provided feedback. Nice to get Techrunched, and see us in VentureBeat, ReadWriteWeb, and and in Seattle’s own John Cook’s blog. VentureBeat’s headline, “Evri launches semantic site to help blaze paths through the Internet” obviously pleased us, as did Lee Graham’s, “Is Evri the Twitter of Digg?” (I am pretty sure that would be a good thing, if I follow the meaning!) Our friends at Freewaregenius hit the nail on the head: “Evri: re-discover the interconnected web”

All of the feedback is great to hear, and we are going to make some small, mid-sprint changes to address some of what we’re hearing. I hope to post a screencast later tonight to provide some guide to the cool stuff we are doing.

The artists at Cohitre have been kind enough to create a pictorial representation of our beta launch. In their words, “The Evri Creature represents genetic diversity and the dinosaur symbolizes the meaning of life.”

Today is a big day for us. We’re pushing our beta into production and asking people to sign-up and try us out. We have a BHAG: to help users make sense of the web’s content by realizing the vision of the web as a place to browse based on the things that interest them. By doing this, we think we can improve the user experience of experiencing content on the web, and help content sites create higher levels of user engagement. You can take a look at the video of my demo at the D: AllThings Digital conference last month to see the user experience in action.

We have all read and heard a lot about social graphs over the last couple of years and know how compelling, and useful, they can be. Evri is building a data graph of the web, using our technology to understand the People, Places and Things in the web content we read every day-and the actions that connect them to each other. We use this natural language-derived grammatical data to make a unique and compelling (well, we think so) user experience. And, even with great semantically-aware data, it’s all about the UI. Evri is building a “data graph” that shows interesting and useful connections to explore about things in the outside world-things that aren’t part of your social graph.

For example, did you know that George Carlin and Jamie Oliver were connected by only four degrees of separation? Or, that you can easily browse a list of everyone that’s “supporting” or “rejecting” Barack Obama and John McCain? Our profile pages allow you to browse content about your interests in a new way-by the types of things they are connected to and the relationships between them. We show a cool graphical representation of the “data graph” of the object in question (here, one of our favorite musicians), and way to browse content by Verbs and Objects (what is Bon Iver “releasing?”) Also, we show other content, including video from the web.

Our home page is just for fun, and captures the Zeitgeist based on the web content we’ve read, and Evri user activity. The Rising and Falling lists show who or what’s getting hot, or cooling off.

One of our goals is to help content sites solve the “related content problem” and the Evri content widget is our first example of this. We know, and extract, the most important things in any article and create a new user experience based on the content you are already reading. No doing multiple follow-up searches to find contextually related content, just read the article and browse our widget for highly relevant content.

This is just the beginning for us, and we already have plans for many new products and improvements. As the White Stripes say: “When you’re in your little room and you’re working on something good / but if it is really good / You’re gonna need a bigger room.” We think we are working on something good, and would love you to help us get to our bigger room. You can do that by signing up for our beta preview, and giving us lots of feedback-good or bad, we need it all.