How RankBrain Changes Entity Search and Google search results?
Google’s RankBrain is a machine learning system that developed to determine that what will the best result for a specific set of a query through some other algorithms will also play their role.Here the queries are processed and refined by using the method of pattern recognition of complex or ambiguous nature queries and then they are connected with specific topics. This facility offers Google to serve the users with better results in many billion search queries per day.
Basically, RankBrain is a query processor and not the ranking factor.The main object to develop and implement this tool is to refine the query results that are found in Entity Search based on Google’s knowledge graph.Though entity search is not a recently introduced term, its addition in the machine learning algorithm is quite new in the market.
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Now some simple questions appear in the mind; what is entity search? How does it work with RanBrain? What is Google’s future with it? To find the answer to these questions, we need to start from a few years earlier with past activities of Google.
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Launching of Hummingbird algorithm was a radically changing movement. It changed Google’s entire system of processing organic queries.The introduction of Hummingbird changed the meaning of search from “strings” to “things”.The algorithm was the result of Google’s efforts to incorporating semantic results into its search engine.It was the way of understanding and processing of natural language so that the search spiders may sense that what a user meant by the text typed in the search box.
On this way, Google can understand and learn the definitions/relationships when the users make a search for a set of terms.It is the place where the concept of machine learning (Rank Brain) comes into existence.This machine makes the best guess based on the user’s perceived intent; and not on user refining query sets.However, still on having RankBrain, Google cannot interpret the meaning of the word as a human can.
The move from “Strings” to “Things”
At present, Google is playing a leading role in surfacing the specific data.It provides various information like a weather report, traffic conditions, movie review, and football match update, without visiting any website, and it is simply displayed on top of the search engine research page.These placements are often based on Google’s enhanced Knowledge Graph, so Google is now moving from “strings” to “Things”.
This movement is very helpful for data-based searches, especially when these data are placed in the knowledge graph.Google’s self-defined micro-moments relating to the various questions such as what, when, how, why, where and who is the information that it provides its users even when they are not completely sure about what they are searching for.
Though Google has excelled at surfacing straightforward and data-based information, there is a downside.It is not very compatible with answering the complex query sets.It is a great limit for Google’s entity search capabilities.But this has some benefits also; when a user types a complex query in the search box, he may find many relevant results even if there is no availability of the exact or highly relevant result.For example, you are searching for an interior decoration agency with nice experience in Italian marble application in your city, if there is no the exact match with your requirement, you may find the other available options in the terms of some other furnishing style or from a nearby town.
Complex queries and their effect on search
The RankBrain system works by using an artificial intelligence and embeds a lot of written languages into mathematical entities (often known as vectors) that can be easily accepted by the computer programs.When RankBrain finds that there is no result that is exactly relevant to the word or phrase typed, its machinery guesses the similar results and filter them to give a sensible result.
Google only understands and searches for the entities.The entities simply are the nouns-person, place, thing, idea or any mental /physical/ geographical condition.Google has a vast database to define the meaning of entities and it uses this for references.It is like a digital encyclopedia of Google and works as in pulling back data points based on entity understanding.
How the entity search exactly works?
Google works on the basis of the words or queries typed in the search box.It is necessary that the typed words are presenting a reasonable and relevant query so that it may not be confused and can show you the clear and most relevant results.Not like a human being, Google can never think of it and can not determine the entities and accurately map their relationship.
When we observe the Google’s search result pages, we find that RanBrain is trying to decipher the intent to present the results.It makes the high efforts to determine the relationships, but sometimes it gives a bunch of many probable results as it is completely confused about your intent.
At this point, there is the need for a system that helps Google to know the things and their relationships.This system has to analyse the relationship of queries and then its probabilities against the pre-known relationships so that it can have a clear understanding of user’s intent.
With the limitation of poor understanding of the intent, it needs a machine that helps in refining for possible matches.This is the point where RankBrain proves its utility in listing the set of probable results for the given query.Now a big thing that really matters a lot-where is Google going?
Google’s future with RankBrain
At present, when Google is busy with experimenting RankBrain with query analysis and getting better results, it has lost its market share.Though it is not in a threatening level, yet its numbers are down.As per the market reports, since Hummingbird was launched, Google has experienced the lost in its market share approximate 3%.The situation refers that the results are not as relevant and improved as they were expected.Some experts are treating this as the worse situation.Now the ball in Google’s field to decide whether it continues performing like a search engine or an answering engine or do them both.
When Google is finally unable to produce a semantic engine, it has built one based on facts. RankBrain is a help here as it refines the search results.The entity search requires the understanding the real meaning of the nouns in the search box and also analyze their relation.This action is supported wisely by RanKBrain.
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As per time, RankBrainer is expected to get better, it will learn the very new entities and their likely relationships between them.That day it will be able to present the better results than today.What is the future of Google and RankBrain in the entity search scenario, this is the question only the time can answer it.