New Panda Update; New Panda Patent Application

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.

Flow chart from the patent showing content scoring based upon phrases

So it sounds like this release of the Panda update could potentially be good news for some sites that were impacted by Panda in the past.

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Google Turns to Deep Learning Classification to Fight Web Spam

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. Unsurprising that Google would start using something like Deep Learning Classification to fight web spam.

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

Deep Learning Classification Patent

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Extracting Semantic Classes from Web Pages and Query Logs

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).

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Google Adds Entity Attributes to its Knowledge Base from Queries

Searchers Queries Teach Google About Entity Attributes

Millions of searches stream into Google every day 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 also help Google populate its knowledge base with more information as well, about entity attributes

When Google collects information about entities – people, places, and things, including products and brands, it might collect facts about those entities and information about entity attributes.

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Google May Use Question Answering to Populate the Knowledge Graph

Added 9/20/2019: The patent application I wrote about in this post was granted on October 23, 2018 – Question answering to populate knowledge base

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 the knowledge graph might be updated when a question answering query is asked of it, and the search system notices that some information is missing from the knowledge graph

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