There are a number of patents from Google, both granted patents and pending patent applications, that describe ways that Google might learn about entities and about facts associated with those by extracting facts for entities from the Web itself instead of relying upon people submitting information to knowledge bases such as Freebase.
We saw Google show off how they could replace their Knowledge Base with a Knowledge Vault, and that would bring a whole new set of extraction approaches with it that have high levels of confidence with them as to how accurate they might be.
It’s hard to tell exactly which approaches Google might be relying upon, and which ones that Google might have introduced through something like a patent that is no longer being used. But, it doesn’t hurt to learn some of the history and some of the approaches that might have been used in the past.
I’m blogging about a patent today that describes an approach that many of us have assumed that Google has been using for years to identify objects or entities and attributes about those and the values that fit those attributes.
Google was officially assigned the pending patent applications from CiiNow last Wednesday (August 27, 2014) in a transaction that was reported as being executed at the end of July.
From searching through the USPTO, I don’t see any other patents assigned to CiiNow, so that appears to have been all they owned. The USPTO assignments don’t include financial details, so that information is unavailable.
The Ciinow.com website appears to be completely unresponsive to visits. The LinkedIn profile of CiiNow Co-Founder and VP of Engineering Devendra (Deven) Raut left CiiNow in 2014 and joined Google as a Tech Biz Dev. It looks to me that Google acquired CiiNow, Inc.
The title from a Google local search 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 engage in Business spam against 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 search listings to disappear completely from Google Maps results. The category Google seems to have placed such listings under is “Fake Business Spam.”
The World Wide Web is a vast resource for information. At the same time it is extremely distributed.
A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many different formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically.
We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author, title) pairs from the World Wide Web.
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: