How RankBrain Changes Entity Search

Columnist Kristine Schachinger provides a handy primer on entity search, explaining how it works and how Google is using its RankBrain machine learning system to make it better.

Kristine Schachinger on October 29, 2015 at 9:31 am

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Earlier this week, news broke about Google’s RankBrain, a machine learning system that, along with other algorithm factors, helps to determine what the best results will be for a specific query set.

Specifically, RankBrain appears to be related to query processing and refinement, using pattern recognition to take complex and/or ambiguous search queries and connect them to specific topics.

This allows Google to serve better search results to users, especially in the case of the hundreds of millions of search queries per day that the search engine has never seen before.

Not to be taken lightly, Google has said that RankBrain is among the most important of the hundreds of ranking signals the algorithm takes into account.

RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked, Corrado said. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query, he said.

(Note: RankBrain is more likely a “query processor” than a true “ranking factor.” It is currently unclear how exactly RankBrain functions as a ranking signal, since those are typically tied to content in some way.)

This is not the only major change to search in recent memory, however. In the past few years, Google has made quite a few important changes to how search works, from algorithm updates to search results page layout. Google has grown and changed into a much different animal than it waspre-Penguin and pre-Panda.

These changes don’t stop at search, either. The company has changed how it is structured. With the new and separate “Alphabet” umbrella, Google is no longer one organism, or even the main one.

Even communication from Google to SEOs and Webmasters has largely gone the way of the dodo. Matt Cutts is no longer the “Google go-to,” and reliable information has become difficult to obtain. So many changes in such a short time. It seems that Google is pushing forward.

Yet, RankBrain is much different from previous changes. RankBrain is an effort to refine the query results of Google’s Knowledge Graph-based entity search. While entity search is not new, the addition of a fully rolled-out machine learning algorithm to these results is only about three months old.

So what is entity search? How does this work with RankBrain? Where is Google going?

To understand the context, we need to go back a few years.

Hummingbird

The launch of the Hummingbird algorithm was a radical change. It was the overhaul of the entire way Google processed organic queries. Overnight, search went from finding “strings” (i.e., strings of letters in a search query) to finding “things” (i.e., entities).

Where did Hummingbird come from? The new Hummingbird algorithm was born out of Google’s efforts to incorporate semantic search into its search engine.

This was supposed to be Google’s foray into not only machine learning, but the understanding and processing of natural language (or NLP). No more need for those pesky keywords — Google would just understand what you meant by what you typed in the search box.

Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search.

Yet we’re two years on, and anyone who uses Google knows the dream of semantic search has not been realized. It’s not that Google meets none of the criteria, but Google falls far short of the full definition.

For instance, it does use databases to define and associate entities. However, a semantic engine would understand how context affects words and then be able to assess and interpret meaning.

Google does not have this understanding. In fact, according to some, Google is simply navigational search — and navigational search is not considered by definition to be semantic in nature.

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.

However, even with RankBrain, Google is not able to interpret meaning as a human would, and that is the Natural Language portion of the semantic definition.

So by definition, Google is NOT a semantic search engine. Then what is it?

The Move From “Strings” to “Things”

[W]e’ve been working on an intelligent model — in geek-speak, a “graph” — that understands real-world entities and their relationships to one another: things, not strings.

Google Official Blog

As mentioned, Google is now very good at surfacing specific data. Need a weather report? Traffic conditions? Restaurant review? Google can provide this information without the need for you to even visit a website, displaying it right on the top of the search results page. Such placements are often based on the Knowledge Graph and are a result of Google’s move from “strings” to “things.”

The move from “strings” to “things” has been great for data-based searches, especially when it places those bits of data in the Knowledge Graph. These bits of data are the ones that typically answer the who, what, where, when, why, and how questions of Google’s self-defined “Micro-Moments.” Google can give users information they may not have even known they wanted at the moment they want it.

However, this push towards entities is not without a downside. While Google has excelled at surfacing straightforward, data-based information, what it hasn’t been doing as well anymore is returning highly relevant answers for complex query sets.

Here, I use “complex queries” to refer simply to queries that do not easily map to an entity, a piece of known data and/or a data attribute — thereby making such queries difficult for Google to “understand.”

As a result, when you search for a set of complex terms, there is a good chance you will get only a few relevant results and not necessarily highly relevant ones. The result is much more a kitchen sink of possibilities than a set of direct answers, but why?