Google Knowledge Graph was
launched on May 16th 2012 and is considered as one of the major discoveries that was made to enhance the existing model of Google search engine enabling it to find the relationship between searched entities and provide the user with the best possible results without having them to navigate to a different website in order to find the answers.
It has been referred to as the "Future of Search" by Google. It enables us to find information about people, places and things. Google is not the first to research in this regard, similar model has been applied before by computational search engine
Wolfram Alpha. But, certainly Google's attempt to categorize people, places and things together is more enhanced and valuable. As such, it becomes important for all of us to have a deeper and better understanding of what the Knowledge Graph holds for us in future?
The future search engine must have the capability to understand the user's intent when searches are made. The Knowledge Graph is an extension towards this possibility. As for example, if any user types the query "Taj Mahal", then he finds information about the monument displayed on the right hand side of the search results along with some other links under "People also searched for" which Google thinks to be relevant and associated with the main object "Taj Mahal". At the bottom of the associated entities, we can find another object under "See results about" which is semantically related to the main search query. However, Knowledge Graph doesn't displays results for all queries. It is still building and soon more and more entities and their relationship would be added in this massive database of semantic world.
How Does Knowledge Graph Works?
It works by understanding the users intent with respect to the search query. People are looking for answers or explore more on a particular topic. Knowledge Graph predicts this behavior of the user and presents the most relevant entitities and their relationships in the form of Graph.
If we move deeper then there are two types of queries processed by the search engines, namely
navigational queries and research queries. Navigational queries corresponds to direct queries where people want to quickly navigate to a particular page and they already have information about the searched query. An example would be "Facebook". As opposed to navigational queries, research queries are much wider and the users do not know where to navigate as they are still searching for information. Knowledge Graph works for research queries and helps users quickly resolve their questions.
Predicting Knowledge Panels with Search Results
The knowledge panels that goes along with the search results are different. Google provides summaries related to the objects identified by the program. This information is displayed in the Knowledge panel. The process is explained below:-
1- Google predicts the factual entity referred by the search query.
2- The Factual entity is searched in the database alongwith semantically related entities.
3- Content is identified based on algorithms and displayed in the knowledge panel.
4- Content includes two items, first referenced from the first resource and second, referenced from the second resource which is different from the first resource. (Source-
Providing Knowledge Panels with Search Results)
What Are It's Sources?
Google makes use of several sources while presenting the data before the user. Some sources are
Wikipedia,
Freebase, CIA World Factbook (For General Data)
and National Library of Medicine, National Institutes of Health, US Food and Drug Administration etc. for data related to medicines and drugs.
How Big is it?
Currently the
Knowledge Graph contains over 570 million objects and over 18 billion facts semantically related with those objects which helps to predict the relationship between objects and facts as per the search query.
Google Will Become An Answer Engine With the Power of the Knowledge Graph
Google is making tremendous improvements in its existing model of Knowledge Graph and is moving towards it's main aim of becoming an answer engine. The graph model of search enables the user to get instant answers to their queries and also discover some new questions which they never thought to ask before. It gives them the power to explore the search insights that most people ask Google. (Apple's
Siri is far ahead of Google in this race)
The Monetization Part
Like every other business, Google also explores sources of revenue with every new research and model it launches for the benefit of the users. Knowledge Graph is no exception to that. Google's head honcho,
Larry Page had said "Knowledge Graph does run on the right hand side where the ads also run". Now you get where all this stuff is going? This was reported by Wordtream's
Elisa Gabbert. (See -
Knowledge Graph Conspiracy Confirmed). It was basically done to navigate users to the right hand side of the search result page where the ads run. This makes organic results less countable and provides the big G with more options of increased revenue.
Corporate Semantic Web Applications
Ever thought of having a customized knowledge graph for your own company? Yes, that's possible. Customized semantic web applications can be created using platforms like Poolparty. An interesting insight towards this possibility has been made by Andreas Blumauer here:-
How to set up a customized Knowledge Graph for a certain company?
SEO Community and Knowledge Graph
What will happen when more and more people will find answers to their queries directly on Google's result page without navigating to another website? What will happen when organic results would loose its current importance due to larger emphasis on Knowledge Graph? These are some of the questions which are being put forward by the Seo community. Google says - only the most accurate content is presented through the Knowledge Graph making it hard to crack into by Black Hat SEO's. (Source:-
memeburn).
Search engine optimizers will need to optimize the content and increase the presence of the content on social websites. Content should be connected to one other in order to form relationship between entities. Google Authorship must be used in order to make it more authoritative in the eyes of Google. The quality of the content now matters much more than the quantity of the content.
Here are some points which the search engine optimizers must follow in order to optimize their presence on Google after the launch of Knowledge Graph:-
1- Optimize your landing page.
2- Study metrics like
Clickthrough Rate and Bounce Rate. Optimize your web pages so that it has a high clickthrough rate and a
low bounce rate.
3- Make use of
Google Authorship as it is the best way to find relationships.
4- Use microdata reviews and stars in order to make content more user interactive.
5- Use Schema markup for local Seo.
6- Use
Rich Snippets for local search.
Moreover, Knowledge graph still uses a tiny portion of the search queries and organic search results are still the way for users to find the best results. There is much more for search engine optimizers to explore and change.
Latest Updates
As per the latest updates, Google has added Medications to its database (See
Google Adds Detailed Information on Medications) and has expanded the graph into movies on Android tablets where it will display information regarding the actors whenever the movie is paused. It will do so by making use of facial recognition technology. (Source-
The verge). Recently, Google bought the startup Wavii which uses natural language processing technology to present filtered results across the web on topics which the users care the most. (Source:-
Google Buys Strartup Wavii)
Also See:-
Google Introduces the Knowledge Graph
How Does Google Applies Semantic Search?
Latent Semantic Indexing
Facebook Graph Search Optimization
Getting Listed on Search Engines
Universal Analytics
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