The title from a Google patent reached out and grabbed me as I was skimming through Google’s patents. It has the kind of title that captures your attention, as a weapon in the war that Google wages against people who might try to spam the search engine.
The title for the patent is Reverse engineering circumvention of spam detection algorithms. The context is local search, where some business owners might be striving to show up in results in places where they don’t actually have a business location, or where heavy competition might convince them that having additional or better entries in Google Maps is going to help their business.
The result of such efforts might be for their local listings to disappear completely from Google Maps results. The category Google seems to have placed such listings under is “Fake Business Spam.”
A few years ago, I presented at SES San Jose and someone asked me what they should be keeping an eye upon in SEO. I told them “named entities.” I was reminded of that conversation as I gave a talk today about named entities and other semantics.
I presented this morning at San Jose McEnery Convention Center at the Semantic Technology and Business Conference (#SemTechBiz2014).
Barbara Starr and I gave a 3 hour Tutorial on Semantic Search to an enthusiastic and engaged audience. We also discussed which might be a better name for the tutorial, “Semantic Search” (the name it had) or Semantic SEO (what do you think?).
Here’s Barbara’s presentation, which is the first half of the tutorial Thanks, Barbara – totally brilliant stuff:
On August 6th, Google announced that https was becoming a ranking signal for Google Search.
I’m not completely sure of the implications of a discovery I made earlier today yet, but I noticed at the USPTO assignment database that Google had been assigned a patent from AT&T in June, which was officially recorded on August 8th, 2014.
The patent is:
I’ve been saying for at least a couple of years that Google’s local search is a proof of concept for the search giant to use on how to find and understand entities.
With local search, Google goes out and looks for a mention of a business on the Web, especially when it it accompanied by geographic location information. It collects and gathers facts related to businesses (entities are people, places, and things) and then it clusters information about the objects it finds to make sure that those mentions across the Web are all referring to the same places.
If you start reading about local search, you’ll see people referring to the importance of consistency in how you present address information for a business, and the same thing is true for entities.
A couple of months ago, I wrote a post about a new patent from Google that was the first Google patent granted to Navneet Panda as an inventor. The patent described a complicated way for Google to judge the quality of websites, and my post was titled Is this Really the Panda Patent?. Simon Penson wrote a followup post at Moz titled The Panda Patent: Brand Mentions Are the Future of Link Building which looked at some other aspects of the patent.
On August 1st, Jayson Demers published a post to Forbes titled Implied Links, Brand Mentions And The Future Of SEO Link Building which covers a lot of the same ground as Simon’s post. I contacted an editor at Forbes and stated that the post plagiarized Simon’s post. Jayson didn’t give me any credit for my post about the patent either, but Simon did.
When Google crawls the Web to collect information about objects or entities, it also collects facts about those entities. These facts are separated into different categories or attributes associated with those entities. For example, a book may have attributes such as an author, a publisher, a year published, a web site it can call home , a genre, and more.
Identifying Entities by their Attributes
A search that includes those attributes can be used to identify the entity the attributes might be associated with.
Google was granted a patent recently that describes how those attributes could be searched within an attribute data store to find the entity. The patent shows how the process described within it might be used to answer some complex queries, and some interactive Answerbox type queries. The issue that this patent addresses can be summed up in a single question:
Years ago, I started referring to search results as recommendations, seeing how they’ve been starting to look more and more like that part of a page at Amazon that says “people who viewed this book also looked at these books.”
When someone searches at a search engine, one of the things they look for in the search results they receive are trustworthy pages (or recommendations) that look (and are) legitimate. How does a search engine deliver pages that are trustworthy?
One way to do that might be to try to boost pages in search results that the search engine feels are more trustworthy – and Google developed a version of Trust Rank to do that with. The inventor of Google’s Trust Rank (which differs from the version that Yahoo invented) is Ramanathan Guha.