Semantic Scholar

Semantic Scholar is a new search engine that uses machine learning to extract concepts. For now its corpus has computer science papers.

Academic Search Engine Grasps for Meaning, Will Knight, MIT Technology Review (Nov 2)

Etzioni says the goal for Semantic Scholar is to go further by giving computers a much deeper understanding of new scientific publications. His team is developing algorithms that will read graphs or charts in papers and try to extract the values presented therein. “We want ultimately to be able to take an experimental paper and say, ‘Okay, do I have to read this paper, or can the computer tell me that this paper showed that this particular drug was highly efficacious?’”

Google’s RankBrain and Entity Analysis

Kristine Schachinger at Search Engine Land tackles the relationship between Google’s RankBrain use machine learning and the entity analysis Google developed in the Knowledge Graph.

How RankBrain Changes Entity Search (Oct 29)

Key section below  – but you will really want to read the entire article yourself.

So while Google can understand known entities and relationships via data definitions, distance and machine learning, it cannot yet understand natural (human) language. It also cannot easily interpret attribute association without additional clarification when those relationships in Google’s repository are weakly correlated or nonexistent. This clarification is often a result of additional user input.

Of course, Google can learn many of these definitions and relationships over time if enough people search for a set of terms. This is where machine learning (RankBrain) comes into the mix. Instead of the user refining query sets, the machine makes a best guess based on the user’s perceived intent.

Google ranks results with RankBrain AI

AI has arrived at Google, after years of  corporate acquisitions and experimental work with machine learning. It’s called RankBrain and it does the following:

RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand. If RankBrain sees a word or phrase it isn’t familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries.

Already RankBrain  is the third most important ranking factor.

See Google Turning Its Lucrative Web Search Over to AI Machines by Jack Clark, Bloomberg, (Oct 26) for a video and description.

Danny Sullivan provides background and details in  FAQ: All About The New Google RankBrain Algorithm [Search Engine Land, Oct 28] t RankBrian is not a new search algorithm: it is one more component (albeit important)  the overall Hummingbird search algorithm introduced a couple of years ago.

Sullivan refers to the Bloomberg article and hazards a guess that the other two top ranking signals being used by Google are 1)  links – still, in spite of problems with these, and 2) words – ie matching on the search terms. Sullivan also mentions that Google has been expanding words for several years – word variants and related words – and that these fit into selecting and ranking results.  Google also employed entity analysis in providing answers through the Knowledge Graph.

Of interest: 15% of the 300 billion queries Google handles each day are new, and being new may lead to some adjustments to algorithms by the staff of search analysts .

Among those can be complex, multi-word queries, also called “long-tail” queries. RankBrain is designed to help better interpret those queries and effectively translate them, behind the scenes in a way, to find the best pages for the searcher.

For those wishing to know more about how RankBrain works with “word vectors”, Sullivan points to a couple of papers.

Greg Finn at Search Engine Land provides another synopsis – AI has officially made it’s way into Google’s search algorithm, here’s what you should know.

Can Bing be far behind in also employing AI for its search results?

Mobile Apps and Voice

Panelists at SMX East discussed the future of search. In reading about the three themes, keep in mind that future is never longer than a year. They are mobile apps and voice – these affect everyone, and attributio – which is a matter of attributing sale of a product to a source or agent.

Looking Ahead: The New Search Landscape, Casie Gilette, Search Engine Land (Oct 1)

Mobile apps, attribution and voice search were the main themes, but of course, the topic of where Yahoo and Bing fit in was brought up. The biggest takeaway was the split of Yahoo/Bing (i.e., Gemini) and how that may end up hurting each company in the long run.

Google’s ranking of TLDs

What does Google do with the new TLDs? Nothing. And now it seems Google doesn’t give preferential ranking to .edu or .gov.

Google Explains How It Handles The New Top Level Domains (TLDs), Barry Schwartz, Search Engine Land (July 21)

In summary, there are no TLDs that Google finds preferential to others; they are all treated equally in rankings. There are some geo-specific TLDs that Google will default to a specific country and use that as an indicator that the website is more important in a specific geographic region. But all TLDs are treated equally.

SEO Periodic Table of Ranking Factors

Danny Sullivan at Search Engine Land has announced a new version of the Periodic Table of SEO Factors (June 1). This is always useful to review as searchers to know the factors that matter for ranking results whether on-the-page or off.

Key in this report are three new factors:

  • Vertical search – meaning that the site must have a variety of resources – images, news, video etc.
  • Direct answers – can be a good thing to be able to provide answers – and construct the page so that Google sees this
  • HTTPS – a secure site using the https protocol

Among the changed factors is structured data – it’s more important to design and designate.

Google Search on a Timeline

This slideshow – A History of Google Algorithm Updates – reminds us of the ways Google search has changed over nearly 15 years. It’s quite stunning and we can only wonder – what next? The timeline was created by DPFOC, an online marketing agency offering SEO services.