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

What's hot

Graph-Based Customer Journey Analytics with Neo4j
Graph-Based Customer Journey Analytics with Neo4jGraph-Based Customer Journey Analytics with Neo4j
Graph-Based Customer Journey Analytics with Neo4j
Neo4j
 
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
Neo4j
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdf
Heather Hedden
 
Topic Modeling - NLP
Topic Modeling - NLPTopic Modeling - NLP
Topic Modeling - NLP
Rupak Roy
 
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data ScienceAI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
Optum
 
Property graph vs. RDF Triplestore comparison in 2020
Property graph vs. RDF Triplestore comparison in 2020Property graph vs. RDF Triplestore comparison in 2020
Property graph vs. RDF Triplestore comparison in 2020
Ontotext
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
Benjamin Raethlein
 
Vector database
Vector databaseVector database
Vector database
Guy Korland
 
Neo4j graph database
Neo4j graph databaseNeo4j graph database
Neo4j graph database
Prashant Bhargava
 
Vector databases and neural search
Vector databases and neural searchVector databases and neural search
Vector databases and neural search
Dmitry Kan
 
Towards an Open Research Knowledge Graph
Towards an Open Research Knowledge GraphTowards an Open Research Knowledge Graph
Towards an Open Research Knowledge Graph
Sören Auer
 
Ontology Engineering for Big Data
Ontology Engineering for Big DataOntology Engineering for Big Data
Ontology Engineering for Big Data
Kouji Kozaki
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
Toine Bogers
 
Introduction to Graph Databases
Introduction to Graph DatabasesIntroduction to Graph Databases
Introduction to Graph Databases
Max De Marzi
 
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataIntroduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Sören Auer
 
Loading your Life into a Vector Database
Loading your Life into a Vector DatabaseLoading your Life into a Vector Database
Loading your Life into a Vector Database
Ben Church
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
Lukas Masuch
 
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptxKnowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
Neo4j
 
Introduction to Knowledge Graphs
Introduction to Knowledge GraphsIntroduction to Knowledge Graphs
Introduction to Knowledge Graphs
mukuljoshi
 

What's hot (20)

Graph-Based Customer Journey Analytics with Neo4j
Graph-Based Customer Journey Analytics with Neo4jGraph-Based Customer Journey Analytics with Neo4j
Graph-Based Customer Journey Analytics with Neo4j
 
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdf
 
Topic Modeling - NLP
Topic Modeling - NLPTopic Modeling - NLP
Topic Modeling - NLP
 
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data ScienceAI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
AI, Knowledge Representation and Graph Databases -
 Key Trends in Data Science
 
Property graph vs. RDF Triplestore comparison in 2020
Property graph vs. RDF Triplestore comparison in 2020Property graph vs. RDF Triplestore comparison in 2020
Property graph vs. RDF Triplestore comparison in 2020
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
 
Vector database
Vector databaseVector database
Vector database
 
Neo4j graph database
Neo4j graph databaseNeo4j graph database
Neo4j graph database
 
Vector databases and neural search
Vector databases and neural searchVector databases and neural search
Vector databases and neural search
 
Towards an Open Research Knowledge Graph
Towards an Open Research Knowledge GraphTowards an Open Research Knowledge Graph
Towards an Open Research Knowledge Graph
 
Boolean Strings by Keyword
Boolean Strings by KeywordBoolean Strings by Keyword
Boolean Strings by Keyword
 
Ontology Engineering for Big Data
Ontology Engineering for Big DataOntology Engineering for Big Data
Ontology Engineering for Big Data
 
Natural Language Processing
Natural Language ProcessingNatural Language Processing
Natural Language Processing
 
Introduction to Graph Databases
Introduction to Graph DatabasesIntroduction to Graph Databases
Introduction to Graph Databases
 
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataIntroduction to the Data Web, DBpedia and the Life-cycle of Linked Data
Introduction to the Data Web, DBpedia and the Life-cycle of Linked Data
 
Loading your Life into a Vector Database
Loading your Life into a Vector DatabaseLoading your Life into a Vector Database
Loading your Life into a Vector Database
 
Enterprise Knowledge Graph
Enterprise Knowledge GraphEnterprise Knowledge Graph
Enterprise Knowledge Graph
 
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptxKnowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptx
 
Introduction to Knowledge Graphs
Introduction to Knowledge GraphsIntroduction to Knowledge Graphs
Introduction to Knowledge Graphs
 

Viewers also liked

Inside Google Knowledge Graph
Inside Google Knowledge GraphInside Google Knowledge Graph
Inside Google Knowledge Graph
Matthew Brown
 
Google knowledge graph
Google knowledge graphGoogle knowledge graph
Google knowledge graph
Akhilesh Joshi
 
Experimenting with Google Knowledge Graph & How Can we Potentially use it in...
 Experimenting with Google Knowledge Graph & How Can we Potentially use it in... Experimenting with Google Knowledge Graph & How Can we Potentially use it in...
Experimenting with Google Knowledge Graph & How Can we Potentially use it in...
Pritesh Patel
 
Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...
Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...
Frank Celler – Processing large-scale graphs with Google(TM) Pregel - NoSQL m...
NoSQLmatters
 
WWII Research Lesson 5
WWII Research Lesson 5WWII Research Lesson 5
WWII Research Lesson 5
John Iona
 
Semantics and linked data at astra zeneca
Semantics and linked data at astra zenecaSemantics and linked data at astra zeneca
Semantics and linked data at astra zeneca
Kerstin Forsberg
 
Future of Search | Yury Lifshits, Yahoo! Research
Future of Search | Yury Lifshits, Yahoo! ResearchFuture of Search | Yury Lifshits, Yahoo! Research
Future of Search | Yury Lifshits, Yahoo! Research
Yury Lifshits
 
Top 10 reporting interview questions with answers
Top 10 reporting interview questions with answersTop 10 reporting interview questions with answers
Top 10 reporting interview questions with answers
kidwellbrandon75
 
Top 9 data analyst interview questions answers
Top 9 data analyst interview questions answersTop 9 data analyst interview questions answers
Top 9 data analyst interview questions answersJobinterviews
 
Knowledge Integration in Practice
Knowledge Integration in PracticeKnowledge Integration in Practice
Knowledge Integration in Practice
Peter Mika
 
Linked data presentation for who umc 21 jan 2015
Linked data presentation for who umc 21 jan 2015Linked data presentation for who umc 21 jan 2015
Linked data presentation for who umc 21 jan 2015Kerstin Forsberg
 
Employing Graph Databases as a Standardization Model towards Addressing Heter...
Employing Graph Databases as a Standardization Model towards Addressing Heter...Employing Graph Databases as a Standardization Model towards Addressing Heter...
Employing Graph Databases as a Standardization Model towards Addressing Heter...
Dippy Aggarwal
 
Semantic Search at Yahoo
Semantic Search at YahooSemantic Search at Yahoo
Semantic Search at Yahoo
Peter Mika
 
Optimising Google's Knowledge Graph - #SMX Munich
Optimising Google's Knowledge Graph - #SMX MunichOptimising Google's Knowledge Graph - #SMX Munich
Optimising Google's Knowledge Graph - #SMX Munich
Jan-Willem Bobbink - Freelance SEO Consultant
 
Leveraging SAP, Hadoop, and Big Data to Redefine Business
Leveraging SAP, Hadoop, and Big Data to Redefine BusinessLeveraging SAP, Hadoop, and Big Data to Redefine Business
Leveraging SAP, Hadoop, and Big Data to Redefine Business
DataWorks Summit
 
Leveraging SAP, Hadoop, and Big Data to Redefine Business
Leveraging SAP, Hadoop, and Big Data to Redefine BusinessLeveraging SAP, Hadoop, and Big Data to Redefine Business
Leveraging SAP, Hadoop, and Big Data to Redefine Business
DataWorks Summit
 
Enterprise knowledge graphs
Enterprise knowledge graphsEnterprise knowledge graphs
Enterprise knowledge graphs
Sören Auer
 
Linked Data efforts for data standards in biopharma and healthcare
Linked Data efforts for data standards in biopharma and healthcareLinked Data efforts for data standards in biopharma and healthcare
Linked Data efforts for data standards in biopharma and healthcare
Kerstin Forsberg
 
Linked Data Tutorial
Linked Data TutorialLinked Data Tutorial
Linked Data Tutorial
Michael Hausenblas
 
Kdd 2014 tutorial bringing structure to text - chi
Kdd 2014 tutorial   bringing structure to text - chiKdd 2014 tutorial   bringing structure to text - chi
Kdd 2014 tutorial bringing structure to text - chi
Barbara Starr
 

Viewers also liked (20)

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

Similar to Google Knowledge Graph

Humming bird doc (1)
Humming bird doc (1)Humming bird doc (1)
Humming bird doc (1)
Manasa Muppala
 
Next-Generation SEO Strategies That Will Future-Proof Your Content
Next-Generation SEO Strategies That Will Future-Proof Your ContentNext-Generation SEO Strategies That Will Future-Proof Your Content
Next-Generation SEO Strategies That Will Future-Proof Your Content
Content Marketing Institute
 
The State of Search
The State of SearchThe State of Search
The State of Search
DemandSphere
 
How Can You Step Ahead In Search Engine Optimization With Data Mining
How Can You Step Ahead In Search Engine Optimization With Data MiningHow Can You Step Ahead In Search Engine Optimization With Data Mining
How Can You Step Ahead In Search Engine Optimization With Data Mining
Kavika Roy
 
Google Analytics 100% (not provided) - what does it mean?
Google Analytics 100% (not provided) - what does it mean? Google Analytics 100% (not provided) - what does it mean?
Google Analytics 100% (not provided) - what does it mean?
Crafted
 
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbakerDynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
searchinsights740
 
A Guide to SEO
A Guide to SEOA Guide to SEO
A Guide to SEO
Laura Kerrigan
 
SEO MYTHS - 2017
SEO MYTHS - 2017SEO MYTHS - 2017
SEO MYTHS - 2017
Filipp Paster
 
18 SEO Myths to Leave Behind in 2017
18 SEO Myths to Leave Behind in 2017 18 SEO Myths to Leave Behind in 2017
18 SEO Myths to Leave Behind in 2017
Tausif Al Hossain
 
Search Engine Optimization
Search Engine OptimizationSearch Engine Optimization
Search Engine Optimization
Shashwat Shankar
 
What is SEO ?
What is SEO ? What is SEO ?
What is SEO ?
skewinfotech
 
Top 9 Search-Driven Analytics Evaluation Criteria
Top 9 Search-Driven Analytics Evaluation CriteriaTop 9 Search-Driven Analytics Evaluation Criteria
Top 9 Search-Driven Analytics Evaluation Criteria
James Capurro
 
Advanced seo-techniques-sourcefile
Advanced seo-techniques-sourcefileAdvanced seo-techniques-sourcefile
Advanced seo-techniques-sourcefile
BEEM - Building Entrepreneurs with Elite Marketing
 
Ai based seo - Edutaus
Ai based seo - EdutausAi based seo - Edutaus
RTC Google Knowledge Graph POV June 2012
RTC Google Knowledge Graph POV June 2012RTC Google Knowledge Graph POV June 2012
RTC Google Knowledge Graph POV June 2012
RTC
 
Internet_Search_Industry_Note
Internet_Search_Industry_NoteInternet_Search_Industry_Note
Internet_Search_Industry_NoteRachit Chowdhary
 
Algorithms and digital marketing
Algorithms and digital marketingAlgorithms and digital marketing
Algorithms and digital marketing
Dhara35514
 
Search V Next Final
Search V Next FinalSearch V Next Final
Search V Next Final
Marianne Sweeny
 

Similar to Google Knowledge Graph (20)

Humming bird doc (1)
Humming bird doc (1)Humming bird doc (1)
Humming bird doc (1)
 
Next-Generation SEO Strategies That Will Future-Proof Your Content
Next-Generation SEO Strategies That Will Future-Proof Your ContentNext-Generation SEO Strategies That Will Future-Proof Your Content
Next-Generation SEO Strategies That Will Future-Proof Your Content
 
The State of Search
The State of SearchThe State of Search
The State of Search
 
How Can You Step Ahead In Search Engine Optimization With Data Mining
How Can You Step Ahead In Search Engine Optimization With Data MiningHow Can You Step Ahead In Search Engine Optimization With Data Mining
How Can You Step Ahead In Search Engine Optimization With Data Mining
 
Google Analytics 100% (not provided) - what does it mean?
Google Analytics 100% (not provided) - what does it mean? Google Analytics 100% (not provided) - what does it mean?
Google Analytics 100% (not provided) - what does it mean?
 
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbakerDynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
Dynamic SERP Ranking: How to Compete in Localized Google Results by @lorenbaker
 
A Guide to SEO
A Guide to SEOA Guide to SEO
A Guide to SEO
 
SEO MYTHS - 2017
SEO MYTHS - 2017SEO MYTHS - 2017
SEO MYTHS - 2017
 
18 SEO Myths to Leave Behind in 2017
18 SEO Myths to Leave Behind in 2017 18 SEO Myths to Leave Behind in 2017
18 SEO Myths to Leave Behind in 2017
 
Search Engine Optimization
Search Engine OptimizationSearch Engine Optimization
Search Engine Optimization
 
What is SEO ?
What is SEO ? What is SEO ?
What is SEO ?
 
Top 9 Search-Driven Analytics Evaluation Criteria
Top 9 Search-Driven Analytics Evaluation CriteriaTop 9 Search-Driven Analytics Evaluation Criteria
Top 9 Search-Driven Analytics Evaluation Criteria
 
E2M
E2ME2M
E2M
 
Google- manendra
Google- manendraGoogle- manendra
Google- manendra
 
Advanced seo-techniques-sourcefile
Advanced seo-techniques-sourcefileAdvanced seo-techniques-sourcefile
Advanced seo-techniques-sourcefile
 
Ai based seo - Edutaus
Ai based seo - EdutausAi based seo - Edutaus
Ai based seo - Edutaus
 
RTC Google Knowledge Graph POV June 2012
RTC Google Knowledge Graph POV June 2012RTC Google Knowledge Graph POV June 2012
RTC Google Knowledge Graph POV June 2012
 
Internet_Search_Industry_Note
Internet_Search_Industry_NoteInternet_Search_Industry_Note
Internet_Search_Industry_Note
 
Algorithms and digital marketing
Algorithms and digital marketingAlgorithms and digital marketing
Algorithms and digital marketing
 
Search V Next Final
Search V Next FinalSearch V Next Final
Search V Next Final
 

Recently uploaded

一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
h7j5io0
 
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
h7j5io0
 
RTUYUIJKLDSADAGHBDJNKSMAL,D
RTUYUIJKLDSADAGHBDJNKSMAL,DRTUYUIJKLDSADAGHBDJNKSMAL,D
RTUYUIJKLDSADAGHBDJNKSMAL,D
cy0krjxt
 
Expert Accessory Dwelling Unit (ADU) Drafting Services
Expert Accessory Dwelling Unit (ADU) Drafting ServicesExpert Accessory Dwelling Unit (ADU) Drafting Services
Expert Accessory Dwelling Unit (ADU) Drafting Services
ResDraft
 
White wonder, Work developed by Eva Tschopp
White wonder, Work developed by Eva TschoppWhite wonder, Work developed by Eva Tschopp
White wonder, Work developed by Eva Tschopp
Mansi Shah
 
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdfPORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
fabianavillanib
 
Top Israeli Products and Brands - Plan it israel.pdf
Top Israeli Products and Brands - Plan it israel.pdfTop Israeli Products and Brands - Plan it israel.pdf
Top Israeli Products and Brands - Plan it israel.pdf
PlanitIsrael
 
Research 20 slides Amelia gavryliuks.pdf
Research 20 slides Amelia gavryliuks.pdfResearch 20 slides Amelia gavryliuks.pdf
Research 20 slides Amelia gavryliuks.pdf
ameli25062005
 
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
smpc3nvg
 
Portfolio.pdf
Portfolio.pdfPortfolio.pdf
Portfolio.pdf
garcese
 
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
9a93xvy
 
Book Formatting: Quality Control Checks for Designers
Book Formatting: Quality Control Checks for DesignersBook Formatting: Quality Control Checks for Designers
Book Formatting: Quality Control Checks for Designers
Confidence Ago
 
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
7sd8fier
 
Can AI do good? at 'offtheCanvas' India HCI prelude
Can AI do good? at 'offtheCanvas' India HCI preludeCan AI do good? at 'offtheCanvas' India HCI prelude
Can AI do good? at 'offtheCanvas' India HCI prelude
Alan Dix
 
CA OFFICE office office office _VIEWS.pdf
CA OFFICE office office office _VIEWS.pdfCA OFFICE office office office _VIEWS.pdf
CA OFFICE office office office _VIEWS.pdf
SudhanshuMandlik
 
National-Learning-Camp 2024 deped....pptx
National-Learning-Camp 2024 deped....pptxNational-Learning-Camp 2024 deped....pptx
National-Learning-Camp 2024 deped....pptx
AlecAnidul
 
Borys Sutkowski portfolio interior design
Borys Sutkowski portfolio interior designBorys Sutkowski portfolio interior design
Borys Sutkowski portfolio interior design
boryssutkowski
 
Top 5 Indian Style Modular Kitchen Designs
Top 5 Indian Style Modular Kitchen DesignsTop 5 Indian Style Modular Kitchen Designs
Top 5 Indian Style Modular Kitchen Designs
Finzo Kitchens
 
Design Thinking Design thinking Design thinking
Design Thinking Design thinking Design thinkingDesign Thinking Design thinking Design thinking
Design Thinking Design thinking Design thinking
cy0krjxt
 
Common Designing Mistakes and How to avoid them
Common Designing Mistakes and How to avoid themCommon Designing Mistakes and How to avoid them
Common Designing Mistakes and How to avoid them
madhavlakhanpal29
 

Recently uploaded (20)

一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
一比一原版(Bolton毕业证书)博尔顿大学毕业证成绩单如何办理
 
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
一比一原版(BU毕业证书)伯恩茅斯大学毕业证成绩单如何办理
 
RTUYUIJKLDSADAGHBDJNKSMAL,D
RTUYUIJKLDSADAGHBDJNKSMAL,DRTUYUIJKLDSADAGHBDJNKSMAL,D
RTUYUIJKLDSADAGHBDJNKSMAL,D
 
Expert Accessory Dwelling Unit (ADU) Drafting Services
Expert Accessory Dwelling Unit (ADU) Drafting ServicesExpert Accessory Dwelling Unit (ADU) Drafting Services
Expert Accessory Dwelling Unit (ADU) Drafting Services
 
White wonder, Work developed by Eva Tschopp
White wonder, Work developed by Eva TschoppWhite wonder, Work developed by Eva Tschopp
White wonder, Work developed by Eva Tschopp
 
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdfPORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
PORTFOLIO FABIANA VILLANI ARCHITECTURE.pdf
 
Top Israeli Products and Brands - Plan it israel.pdf
Top Israeli Products and Brands - Plan it israel.pdfTop Israeli Products and Brands - Plan it israel.pdf
Top Israeli Products and Brands - Plan it israel.pdf
 
Research 20 slides Amelia gavryliuks.pdf
Research 20 slides Amelia gavryliuks.pdfResearch 20 slides Amelia gavryliuks.pdf
Research 20 slides Amelia gavryliuks.pdf
 
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
一比一原版(Brunel毕业证书)布鲁内尔大学毕业证成绩单如何办理
 
Portfolio.pdf
Portfolio.pdfPortfolio.pdf
Portfolio.pdf
 
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
一比一原版(CITY毕业证书)谢菲尔德哈勒姆大学毕业证如何办理
 
Book Formatting: Quality Control Checks for Designers
Book Formatting: Quality Control Checks for DesignersBook Formatting: Quality Control Checks for Designers
Book Formatting: Quality Control Checks for Designers
 
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
一比一原版(MMU毕业证书)曼彻斯特城市大学毕业证成绩单如何办理
 
Can AI do good? at 'offtheCanvas' India HCI prelude
Can AI do good? at 'offtheCanvas' India HCI preludeCan AI do good? at 'offtheCanvas' India HCI prelude
Can AI do good? at 'offtheCanvas' India HCI prelude
 
CA OFFICE office office office _VIEWS.pdf
CA OFFICE office office office _VIEWS.pdfCA OFFICE office office office _VIEWS.pdf
CA OFFICE office office office _VIEWS.pdf
 
National-Learning-Camp 2024 deped....pptx
National-Learning-Camp 2024 deped....pptxNational-Learning-Camp 2024 deped....pptx
National-Learning-Camp 2024 deped....pptx
 
Borys Sutkowski portfolio interior design
Borys Sutkowski portfolio interior designBorys Sutkowski portfolio interior design
Borys Sutkowski portfolio interior design
 
Top 5 Indian Style Modular Kitchen Designs
Top 5 Indian Style Modular Kitchen DesignsTop 5 Indian Style Modular Kitchen Designs
Top 5 Indian Style Modular Kitchen Designs
 
Design Thinking Design thinking Design thinking
Design Thinking Design thinking Design thinkingDesign Thinking Design thinking Design thinking
Design Thinking Design thinking Design thinking
 
Common Designing Mistakes and How to avoid them
Common Designing Mistakes and How to avoid themCommon Designing Mistakes and How to avoid them
Common Designing Mistakes and How to avoid them
 

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.