SlideShare a Scribd company logo
1
 Introduction
Google introduced a new feature, which represents a
substantial extension to how their search engine
presents information and marks a significant
departure from some of the principles that have
underpinned their conceptual and technological
approach since 1998.
Zinavo Technologies
 Knowledge Graph display was added to Google's search engine in
2012, starting in the United States, having been announced on
May 16, 2012.
 The information in the Knowledge Graph is derived from many sources,
including the CIA World Factbook, Freebase, and Wikipedia. The feature
is similar in intent to answer engines such as Ask Jeeves and Wolfram
Alpha.
 As of 2012, its semantic network contained over 570 million objects and
more than 18 billion facts about and relationships between different
objects that are used to understand the meaning of
the keywords entered for the search.
The “knowledge graph” basically adds a
layer to the search engine that is based on
formal knowledge modeling rather than
word statistics (relevance measures) and
link analysis (authority measures).
“The Knowledge Graph enables you to search for things,
people or places that Google knows about—landmarks,
celebrities, cities, sports teams, buildings, geographical
features, movies, celestial objects, works of art and more—
and instantly get information that’s relevant to your query.
This is a critical first step towards building the next
generation of search, which taps into the collective
intelligence of the web and understands the world a bit more
like people do.”
 The Knowledge Graph is a knowledge base used
by Google to enhance its search engine's search results
with semantic-search information gathered from a wide
variety of sources.
 It provides structured and detailed information about the
topic in addition to a list of links to other sites.
 This is useful for a lot of reasons, but by far the most
important reason is because it facilitates human trends. You
can’t go to a website and easily find spider webs of
information that all, in some way, relate to each other for
the sole reason that only a product as big as Google’s search
could possibly hope to organize that information!
The goal is that users would be able to
use this information to resolve their
query without having to navigate to
other sites and assemble the
information themselves.
Johanna Wright calls the move “from
an information engine to a knowledge
engine”
 The “knowledge graph” basically adds a layer to the search
engine that is based on formal knowledge modelling rather
than word statistics (relevance measures) and link analysis
(authority measures).
 Google is "enhancing" informational queries by serving up
some of the information you might find on reference sites
right in the SERP, as they have done in the past with weather
reports, movie times, flight information, and other data,
essentially removing the need for a Google user to click
through to another site.
For example, if I were looking for information on Matt Cutts, I
would almost certainly have to click one of the results to
get rich information with any real value. All the Knowledge
Graph here tells me is a) what he looks like b) his job c)
where he went to school and d) some other people who
are sort of associated with him. The Wikipedia page or his
own website would have much more richness of detail.
 Knowledge Graph is providing me with some level of detail
(beyond the snippets, which already reveal some
information) before I click on any results. It's also making the
SERP more visual and more attractive.
 The knowledge Graph does run on the right hand side
where the ads also run. Prospect there could be some
short-term impacts on that, but I think the primary thing is
getting people better answers is really good for our
business.
 Shopping about product listing ads I think that for us in the
early stages of that. We just rolled out Google Shopping
 It seems crystal clear that he just acknowledged the fact
that Knowledge Graph goes where the ads go. They are very
aware of the placement and implications
TomCruisemoviesvaiolaptops
 The Knowledge Graph is beneficial to on-site content
strategies, especially post-Google’s Panda update
 This specificity is great for users. They are able to find what
they are looking for much more efficiently without the noise
caused by results for words with different meanings.
 There is also an inherent benefit to advertisers. No longer do
they have to compete with irrelevant search results for
valuable search engine real estate. With more refined results,
there are fewer pages that can potentially outrank an
advertiser’s brand.
 Before Panda, it was typical for content strategies to include
developing unique pages around all targeted keywords. In
order to rank for two different phrases that have the same
meaning two pages would be created, each optimized for
one of the synonymous phrases.
 The problem is that since the two content pieces are about
the exact same topic, the content would be very similar and
not have any added value to the user.
 The fact that the Panda update made this practice essentially
obsolete by flagging the similar pages as “thin content” and
devaluing them posed a great threat to SEO.
 The Google Knowledge Graph is a theory involving the
semantic Web. Instead of a search engine keying into certain
words, it actually interprets what each words means, based
upon the other words entered into the search engine.
 Many words have two or more meanings, and since a search
engine is just a computer program it cannot tell the
difference between one and the other.
 The Google Knowledge Graph theory is that the search
engine will be able to interpret the meaning of words from
other words in the search bar, so that the search
engine results are not as distorted.
 Google suggestions gives webmasters an idea of what people
are going to run a search for. This makes it good for SEO,
because all they need to do is run a few Google searches, and
they will be able to what people are going to click for
suggestions.
 The suggestions may be used as inspiration for the
webmaster’s SEO.
 If the Google Knowledge Graph is actually doing its job, then your
traffic numbers should drop a little, but your more targeted traffic should
go up. This is because people are not typing your keywords into the
search engine and getting unrelated results.
 Long tailed keywords are surely going to become more powerful as the
Google Knowledge Graph becomes more sophisticated.
 The semantic feature of the Google Knowledge Graph means that the
words surrounding a keyword are going to affect the search results.
 Long tailed keywords have words surrounding other words, and so are
surely going to become more powerful than keywords that stand alone.
Google Knowledge Graph

More Related Content

PDF
Introduction to Knowledge Graphs and Semantic AI
PDF
Enterprise Knowledge Graph
PPTX
Knowledge Graph Introduction
PDF
Introduction to Spark with Python
PDF
The three layers of a knowledge graph and what it means for authoring, storag...
PDF
Data Modeling with Neo4j
PDF
Knowledge Graphs - The Power of Graph-Based Search
PDF
PySpark in practice slides
Introduction to Knowledge Graphs and Semantic AI
Enterprise Knowledge Graph
Knowledge Graph Introduction
Introduction to Spark with Python
The three layers of a knowledge graph and what it means for authoring, storag...
Data Modeling with Neo4j
Knowledge Graphs - The Power of Graph-Based Search
PySpark in practice slides

What's hot (20)

PPTX
Introduction to Graph Databases
PDF
PySpark Best Practices
PDF
Spark SQL
PDF
Apache Arrow: High Performance Columnar Data Framework
PDF
Introduction to PySpark
PPTX
EY: Why graph technology makes sense for fraud detection and customer 360 pro...
PDF
Querying the Wikidata Knowledge Graph
PDF
SQL on everything, in memory
PPTX
SPARQL Cheat Sheet
PDF
Databricks + Snowflake: Catalyzing Data and AI Initiatives
PPTX
Databricks Platform.pptx
PDF
Slides: Knowledge Graphs vs. Property Graphs
PPTX
Introduction to ML with Apache Spark MLlib
PDF
Change Data Feed in Delta
PDF
Moving to Databricks & Delta
PDF
Snowflake Data Science and AI/ML at Scale
PDF
Apache Iceberg: An Architectural Look Under the Covers
PDF
Danish Business Authority: Explainability and causality in relation to ML Ops
PPTX
Transforming BT’s Infrastructure Management with Graph Technology
PDF
Introduction of Knowledge Graphs
Introduction to Graph Databases
PySpark Best Practices
Spark SQL
Apache Arrow: High Performance Columnar Data Framework
Introduction to PySpark
EY: Why graph technology makes sense for fraud detection and customer 360 pro...
Querying the Wikidata Knowledge Graph
SQL on everything, in memory
SPARQL Cheat Sheet
Databricks + Snowflake: Catalyzing Data and AI Initiatives
Databricks Platform.pptx
Slides: Knowledge Graphs vs. Property Graphs
Introduction to ML with Apache Spark MLlib
Change Data Feed in Delta
Moving to Databricks & Delta
Snowflake Data Science and AI/ML at Scale
Apache Iceberg: An Architectural Look Under the Covers
Danish Business Authority: Explainability and causality in relation to ML Ops
Transforming BT’s Infrastructure Management with Graph Technology
Introduction of Knowledge Graphs
Ad

Viewers also liked (20)

PPTX
Inside Google Knowledge Graph
PPT
Google knowledge graph
PDF
Experimenting with Google Knowledge Graph & How Can we Potentially use it in...
PDF
Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...
PPT
WWII Research Lesson 5
PDF
Semantics and linked data at astra zeneca
PPT
Future of Search | Yury Lifshits, Yahoo! Research
PPTX
Top 10 reporting interview questions with answers
DOC
Top 9 data analyst interview questions answers
PPTX
Knowledge Integration in Practice
PPTX
Linked data presentation for who umc 21 jan 2015
PDF
Employing Graph Databases as a Standardization Model towards Addressing Heter...
PPTX
Semantic Search at Yahoo
PDF
Optimising Google's Knowledge Graph - #SMX Munich
PPTX
Leveraging SAP, Hadoop, and Big Data to Redefine Business
PPTX
Leveraging SAP, Hadoop, and Big Data to Redefine Business
PPTX
Enterprise knowledge graphs
PPTX
Linked Data efforts for data standards in biopharma and healthcare
PDF
Linked Data Tutorial
PPTX
Kdd 2014 tutorial bringing structure to text - chi
Inside Google Knowledge Graph
Google knowledge graph
Experimenting with Google Knowledge Graph & How Can we Potentially use it in...
Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...
WWII Research Lesson 5
Semantics and linked data at astra zeneca
Future of Search | Yury Lifshits, Yahoo! Research
Top 10 reporting interview questions with answers
Top 9 data analyst interview questions answers
Knowledge Integration in Practice
Linked data presentation for who umc 21 jan 2015
Employing Graph Databases as a Standardization Model towards Addressing Heter...
Semantic Search at Yahoo
Optimising Google's Knowledge Graph - #SMX Munich
Leveraging SAP, Hadoop, and Big Data to Redefine Business
Leveraging SAP, Hadoop, and Big Data to Redefine Business
Enterprise knowledge graphs
Linked Data efforts for data standards in biopharma and healthcare
Linked Data Tutorial
Kdd 2014 tutorial bringing structure to text - chi
Ad

Similar to Google Knowledge Graph (20)

PDF
Humming bird doc (1)
PDF
Next-Generation SEO Strategies That Will Future-Proof Your Content
PDF
The State of Search
PDF
How Can You Step Ahead In Search Engine Optimization With Data Mining
PDF
Google Analytics 100% (not provided) - what does it mean?
PDF
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
PDF
A Guide to SEO
PDF
SEO MYTHS - 2017
PDF
18 SEO Myths to Leave Behind in 2017
PDF
Search Engine Optimization
PDF
In 2025, SEO trends will see a significant shift towards AI-powered tools
PDF
Search Engine Jornal,SEO Trends 2025 Ebook
PPTX
What is SEO ?
PDF
Top 9 Search-Driven Analytics Evaluation Criteria
DOCX
PPT
Google- manendra
DOC
Advanced seo-techniques-sourcefile
PDF
The Role of Entities in Search Engines.pdf
PDF
RTC Google Knowledge Graph POV June 2012
 
Humming bird doc (1)
Next-Generation SEO Strategies That Will Future-Proof Your Content
The State of Search
How Can You Step Ahead In Search Engine Optimization With Data Mining
Google Analytics 100% (not provided) - what does it mean?
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
A Guide to SEO
SEO MYTHS - 2017
18 SEO Myths to Leave Behind in 2017
Search Engine Optimization
In 2025, SEO trends will see a significant shift towards AI-powered tools
Search Engine Jornal,SEO Trends 2025 Ebook
What is SEO ?
Top 9 Search-Driven Analytics Evaluation Criteria
Google- manendra
Advanced seo-techniques-sourcefile
The Role of Entities in Search Engines.pdf
RTC Google Knowledge Graph POV June 2012
 

Recently uploaded (20)

PDF
Emailing DDDX-MBCaEiB.pdf DDD_Europe_2022_Intro_to_Context_Mapping_pdf-165590...
PPTX
Entrepreneur intro, origin, process, method
PPTX
building Planning Overview for step wise design.pptx
PPTX
joggers park landscape assignment bandra
PDF
Trusted Executive Protection Services in Ontario — Discreet & Professional.pdf
PDF
Chalkpiece Annual Report from 2019 To 2025
PDF
Skskkxiixijsjsnwkwkaksixindndndjdjdjsjjssk
PPTX
CLASS_11_BUSINESS_STUDIES_PPT_CHAPTER_1_Business_Trade_Commerce.pptx
PDF
UNIT 1 Introduction fnfbbfhfhfbdhdbdto Java.pptx.pdf
PPTX
Media And Information Literacy for Grade 12
PDF
Integrated-2D-and-3D-Animation-Bridging-Dimensions-for-Impactful-Storytelling...
PPTX
ANATOMY OF ANTERIOR CHAMBER ANGLE AND GONIOSCOPY.pptx
PPTX
BSCS lesson 3.pptxnbbjbb mnbkjbkbbkbbkjb
PPT
pump pump is a mechanism that is used to transfer a liquid from one place to ...
PPTX
EDP Competencies-types, process, explanation
PDF
SEVA- Fashion designing-Presentation.pdf
PPTX
Special finishes, classification and types, explanation
PDF
Key Trends in Website Development 2025 | B3AITS - Bow & 3 Arrows IT Solutions
PDF
Design Thinking - Module 1 - Introduction To Design Thinking - Dr. Rohan Dasg...
PDF
Phone away, tabs closed: No multitasking
Emailing DDDX-MBCaEiB.pdf DDD_Europe_2022_Intro_to_Context_Mapping_pdf-165590...
Entrepreneur intro, origin, process, method
building Planning Overview for step wise design.pptx
joggers park landscape assignment bandra
Trusted Executive Protection Services in Ontario — Discreet & Professional.pdf
Chalkpiece Annual Report from 2019 To 2025
Skskkxiixijsjsnwkwkaksixindndndjdjdjsjjssk
CLASS_11_BUSINESS_STUDIES_PPT_CHAPTER_1_Business_Trade_Commerce.pptx
UNIT 1 Introduction fnfbbfhfhfbdhdbdto Java.pptx.pdf
Media And Information Literacy for Grade 12
Integrated-2D-and-3D-Animation-Bridging-Dimensions-for-Impactful-Storytelling...
ANATOMY OF ANTERIOR CHAMBER ANGLE AND GONIOSCOPY.pptx
BSCS lesson 3.pptxnbbjbb mnbkjbkbbkbbkjb
pump pump is a mechanism that is used to transfer a liquid from one place to ...
EDP Competencies-types, process, explanation
SEVA- Fashion designing-Presentation.pdf
Special finishes, classification and types, explanation
Key Trends in Website Development 2025 | B3AITS - Bow & 3 Arrows IT Solutions
Design Thinking - Module 1 - Introduction To Design Thinking - Dr. Rohan Dasg...
Phone away, tabs closed: No multitasking

Google Knowledge Graph

  • 1. 1
  • 2.  Introduction Google introduced a new feature, which represents a substantial extension to how their search engine presents information and marks a significant departure from some of the principles that have underpinned their conceptual and technological approach since 1998. Zinavo Technologies
  • 3.  Knowledge Graph display was added to Google's search engine in 2012, starting in the United States, having been announced on May 16, 2012.  The information in the Knowledge Graph is derived from many sources, including the CIA World Factbook, Freebase, and Wikipedia. The feature is similar in intent to answer engines such as Ask Jeeves and Wolfram Alpha.  As of 2012, its semantic network contained over 570 million objects and more than 18 billion facts about and relationships between different objects that are used to understand the meaning of the keywords entered for the search.
  • 4. The “knowledge graph” basically adds a layer to the search engine that is based on formal knowledge modeling rather than word statistics (relevance measures) and link analysis (authority measures).
  • 5. “The Knowledge Graph enables you to search for things, people or places that Google knows about—landmarks, celebrities, cities, sports teams, buildings, geographical features, movies, celestial objects, works of art and more— and instantly get information that’s relevant to your query. This is a critical first step towards building the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do.”
  • 6.  The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources.  It provides structured and detailed information about the topic in addition to a list of links to other sites.
  • 7.  This is useful for a lot of reasons, but by far the most important reason is because it facilitates human trends. You can’t go to a website and easily find spider webs of information that all, in some way, relate to each other for the sole reason that only a product as big as Google’s search could possibly hope to organize that information!
  • 8. The goal is that users would be able to use this information to resolve their query without having to navigate to other sites and assemble the information themselves. Johanna Wright calls the move “from an information engine to a knowledge engine”
  • 9.  The “knowledge graph” basically adds a layer to the search engine that is based on formal knowledge modelling rather than word statistics (relevance measures) and link analysis (authority measures).  Google is "enhancing" informational queries by serving up some of the information you might find on reference sites right in the SERP, as they have done in the past with weather reports, movie times, flight information, and other data, essentially removing the need for a Google user to click through to another site.
  • 10. For example, if I were looking for information on Matt Cutts, I would almost certainly have to click one of the results to get rich information with any real value. All the Knowledge Graph here tells me is a) what he looks like b) his job c) where he went to school and d) some other people who are sort of associated with him. The Wikipedia page or his own website would have much more richness of detail.
  • 11.  Knowledge Graph is providing me with some level of detail (beyond the snippets, which already reveal some information) before I click on any results. It's also making the SERP more visual and more attractive.
  • 12.  The knowledge Graph does run on the right hand side where the ads also run. Prospect there could be some short-term impacts on that, but I think the primary thing is getting people better answers is really good for our business.  Shopping about product listing ads I think that for us in the early stages of that. We just rolled out Google Shopping  It seems crystal clear that he just acknowledged the fact that Knowledge Graph goes where the ads go. They are very aware of the placement and implications
  • 14.  The Knowledge Graph is beneficial to on-site content strategies, especially post-Google’s Panda update  This specificity is great for users. They are able to find what they are looking for much more efficiently without the noise caused by results for words with different meanings.  There is also an inherent benefit to advertisers. No longer do they have to compete with irrelevant search results for valuable search engine real estate. With more refined results, there are fewer pages that can potentially outrank an advertiser’s brand.
  • 15.  Before Panda, it was typical for content strategies to include developing unique pages around all targeted keywords. In order to rank for two different phrases that have the same meaning two pages would be created, each optimized for one of the synonymous phrases.  The problem is that since the two content pieces are about the exact same topic, the content would be very similar and not have any added value to the user.  The fact that the Panda update made this practice essentially obsolete by flagging the similar pages as “thin content” and devaluing them posed a great threat to SEO.
  • 16.  The Google Knowledge Graph is a theory involving the semantic Web. Instead of a search engine keying into certain words, it actually interprets what each words means, based upon the other words entered into the search engine.  Many words have two or more meanings, and since a search engine is just a computer program it cannot tell the difference between one and the other.  The Google Knowledge Graph theory is that the search engine will be able to interpret the meaning of words from other words in the search bar, so that the search engine results are not as distorted.
  • 17.  Google suggestions gives webmasters an idea of what people are going to run a search for. This makes it good for SEO, because all they need to do is run a few Google searches, and they will be able to what people are going to click for suggestions.  The suggestions may be used as inspiration for the webmaster’s SEO.
  • 18.  If the Google Knowledge Graph is actually doing its job, then your traffic numbers should drop a little, but your more targeted traffic should go up. This is because people are not typing your keywords into the search engine and getting unrelated results.  Long tailed keywords are surely going to become more powerful as the Google Knowledge Graph becomes more sophisticated.  The semantic feature of the Google Knowledge Graph means that the words surrounding a keyword are going to affect the search results.  Long tailed keywords have words surrounding other words, and so are surely going to become more powerful than keywords that stand alone.