Google’s Pierre Far announced on his Google+ page that Google was releasing a new Panda update that supposedly included some new signals that could potentially help “identify low-quality content more precisely.”
The Google+ post also tells us that this change can help lead to a “greater diversity of high-quality small- and medium-sized sites ranking higher, which is nice.”
A new patent application shows off a quality scoring approach for content, based upon phrases. More on that patent filing below, but it might have something to do with this update.
Continue reading New Panda Update; New Panda Patent Application
It can be difficult classifying a query for a search engine based upon the query itself.
For example, you could classify the query “lincoln” based upon:
President Abraham Lincoln
The location, Lincoln,Nebraska
Continue reading Using Query User Data to Classify Queries
In the past few years, Google has been busy building what has become known as the Google Brain team, which started out by having its deep learning approach watching videos until it learned to recognize cats.
Google has been hiring a number of people to add to the abilities of their deep learning team, including a pricy acqui-hire in the UK earlier this year, as described in More on DeepMind: AI Startup to Work Directly With Google’s Search Team
Web Spam Classification Patent
Continue reading Google Turns to Deep Learning Classification to Fight Web Spam
In creating a knowledge base, there seem to be a number of approaches that can be used to supply entities and facts from sources like web pages and query logs.
In my last post, I wrote about how search queries might be used, along with linguistic patterns, to extract attributes about facts from those search queries, as described in a patent titled Inferring attributes from search queries.
A Microsoft paper from 2009, Named Entity Recognition in Query, tells of a manual analysis they performed of 1,000 queries, and told us that 70% of those queries contained named entities.
So entities do appear in queries, and Google receives a lot of queries a day (as does Microsoft and Yahoo).
Continue reading Extracting Semantic Classes and Corresponding Instances from Web Pages and Query Logs
Millions of searches stream into Google everyday as people try to meet their informational and situational needs. But those searches don’t disappear after the searches. They provide Google with some very interesting and useful information in return. For instance, they tell Google what people are interested in real time – right at this moment.
Those queries can help Google populate its knowledge base with more information as well.
When Google collects information about entities – people, places, and things, including products and brands, it might collect information about entities as well as information about attributes associated with those entities.
A couple of days ago, the Google Research Blog told us about how it might include that kind of factual information in search results, what they called Structured Snippets. In that post, Google gave us the news that Google finds information like this from Tables across the web.
Continue reading How Google May Add to its Knowledge Base with Entities and Attributes from Search Queries
Entities change all the time, and facts about them do as well. Imagine when Derek Jeter retires from playing baseball, that he might decide to become a coach. Or Tom Cruise acting in a new movie, and deciding to try directing it and producing it as well. And Scotland decides whether or not it should be independent of the UK after 300 years.
What we think of entities can change over time, when it comes to the type of entity they are, and the facts associated with them. When populations of places change, and they do on a regular basis, how does that information get updated? And unfortunately, sometimes some information never quite makes it to Google’s knowledge base.
A patent application published last week looks at some ways that a knowledge base might be updated when a question answering query is asked of it, and the search system notices that some information is missing.
Continue reading How Google Might Fill in Missing and Incorrect Data in its Knowledge Graph
When Google introduced us to the knowledge graph, it also introduced us to pictures and the possibility of other kinds of rich content (video, audio, etc.) in those knowledge panels, and pictorial lists displayed in carousels at the top of pages in response to a query, such as “What is the tallest building in the World?”
A Google patent granted a couple of weeks ago, describes how Google processes search system queries, and might display knowledge graph answers to questions that include images. Here’s where they introduced carousels, in their page on the Knowledge Graph:
Continue reading Images in Question Answers, Carousels, and Knowledge Panels at Google
This is officially part of the story I’m telling in a presentation I prepared for SMX East, in a couple of weeks in New York. The name of the session I’m in is “Hummingbird and the Entity Revolution,” which reminds me of a Prince song from the 1980s.
The story starts off with a student given a tour by another student whom he gets into a fight with. They liked fighting with each other, and ended up becoming close friends. They studied together, and when their supervising professor went away to Japan for a year, they stopped working on their advanced degrees, and played on the internet instead. They created something they called Backrub. It later had its name changed to Google, and many people in the present day think it is the internet.
On March 10, 1999, Sergey Brin filed a “Miscellaneous Incoming Letter” (this is what it is described as in the USPTO’s PAIR database). It’s a provisional patent titled Extracting Patterns and Relations from Scattered Databases Such as the World Wide Web (pdf) (Skip quickly past the first couple of pages. It becomes much more legible from the third page on.)
Continue reading Google’s First Semantic Search Invention was Patented in 1999