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The Knowledge Graph –
A Paradigm Shift In Search
Ric Rodriguez, SEO Consultant
Comment ?
- Remplissez le formulaire mis à disposition en
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- Assistez au tirage au sort qui aura lieu à la fin du
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2019 - SEO Consultant, Yext
2018 - SEO Director, iProspect
2016 - SEO Director, Croud
2016 - SEO Strategist, Three Whiskey
2014 - SEO Analyst, iCrossing
Technology has enabled people
to look for info in different ways
Speed to adoption is increasing
Time to reach 50m users
46
years
50
years
62
years
64
years
70
0
Sources: Visual Capitalist & Interactive Schools
Speed to adoption is increasing
Time to reach 50m users
46
years
50
years
62
years
64
years
12
years
22
years
28
years
70
0
Sources: Visual Capitalist & Interactive Schools
Speed to adoption is increasing
Time to reach 50m users
12
years 7
years 10
months
19
days
22
years
28
years
46
years
50
years
62
years
64
years
70
0
Sources: Visual Capitalist & Interactive Schools
The rise of different formats has
complicated the landscape too
Shifting channels of engagement
Time spend by device / day6h
0h
2016201520142012 201720132011201020092008
3h
Sources: Kleiner Perkins
Mobile
Other
Desktop
Shifting channels of engagement
6h
0h
2016201520142012 201720132011201020092008
3h
Sources: Kleiner Perkins
Other
Desktop
Mobile
Time spend by device / day
Shifting channels of engagement
Smart speaker sales (projection)300m
0m
2024202320222020 202520212019201820172016
150m
Sources: Loup Ventures / Voicebot.ai
37m
units
9m
units
73m
units52m
units
Shifting channels of engagement
Smart speaker sales (projection)300m
0m
2024202320222020 202520212019201820172016
150m
Sources: Loup Ventures / Voicebot.ai
37m
units
9m
units
73m
units52m
units
101m
units
129m
units
172m
units
205m
units
233m
units
291m
units
People have better access to
information than ever before
Search has changed over time
Source: Live Internet Stats
1998 2018
Discovery
# Of Websites In Existence
17m 29m 52m
Source: Live Internet Stats
1998 2018
Discovery Prioritisation
# Of Websites In Existence
17m 29m 52m 86m
172m 207m
697m
Search has changed over time
Source: Live Internet Stats
1998 2018
Discovery Prioritisation Answers
# Of Websites In Existence
17m 29m 52m 86m
172m 207m
697m
969m 1.04bn
1.6bn
Search has changed over time
This fundamental change in user
behaviour is translating into data
Paid Clicks (4.5%)
Less people are going to websites
Source: SparkToro
Organic Clicks (45.5%)
Paid Clicks (4.5%)
Less people are going to websites
Source: SparkToro
Zero-Click
Searches (50%)
Organic Clicks (45.5%)
Paid Clicks (4.5%)
Less people are going to websites
Source: SparkToro
Connecting with users at the point
of intent is increasingly a challenge
Search is more than your website
how far is the moon?_
how far is the moon?_
Search
Answers
Websites
Meta
Search
Voice
Search
Social
Media
Apps /
PWAs
Search
Results
Chat /
Message
AR / VR
Search is more than your website
how far is the moon?_
Search
Answers
Websites
Meta
Search
Voice
Search
Social
Media
Apps /
PWAs
Search
Results
Chat /
Message
AR / VR
User Interface
Search is more than your website
how far is the moon?_
Search
Answers
Websites
Meta
Search
Voice
Search
Social
Media
Apps /
PWAs
Search
Results
Chat /
Message
AR / VR
Machine Learning
User Interface
Search is more than your website
how far is the moon?_
Search
Answers
Websites
Meta
Search
Voice
Search
Social
Media
Apps /
PWAs
Search
Results
Chat /
Message
AR / VR
Machine Learning
User Interface
Knowledge Graph
Search is more than your website
how far is the moon?_
Search
Answers
Websites
Meta
Search
Voice
Search
Social
Media
Apps /
PWAs
Search
Results
Chat /
Message
AR / VR
Machine Learning
User Interface
Knowledge Graph
Search is more than your website
For many, this is a new idea – but it’s
how search has worked for a while
To organise the world’s information
High
Crawling
To organise the world’s information
High
Crawling Parsing
To organise the world’s information
IndexingCrawling Parsing
To organise the world’s information
High
Indexing QueryingCrawling Parsing
To organise the world’s information
Indexing Querying ServingCrawling Parsing
To organise the world’s information
ServingCrawling Indexing QueryingParsing
To organise the world’s information
Indexing Querying ServingCrawling Parsing
A knowledge graph enables us to
make sense of complicated data
An intro to graph (network) theory
Y
X
An intro to graph (network) theory
Y
X
(Although its
not unrelated)
An intro to graph (network) theory
A
D
B
C
E
Y
X
An intro to graph (network) theory
A
D
B
C
E
Y
X
Vertices / Nodes
An intro to graph (network) theory
A
D
B
C
E
Y
X
Edges (connections)
An intro to graph (network) theory
A
D
B
C
E
Y
X
Pizza
Pasta
Cheese
Italian Cuisine
Penne
However, it’s the relationships and
attributes that make the difference
Linking data together with meaning
Margherita
Pizza
Italian
Cuisine
Mozzarella
Cheese
Linking data together with meaning
ObjectSubject
Predicate
Relationships can “direct” either way
Linking data together with meaning
Margherita
Pizza
Mozzarella
Cheese
Italian
Cuisine
Used In: Part Of:
Linking data together with meaning
ObjectSubject
Predicate
Attributes:
Value : Pair
Value : Pair
Attributes:
Value : Pair
Value : Pair
Each node can have
descriptive attributes
Linking data together with meaning
Margherita
Pizza
Mozzarella
Cheese
Italian
Cuisine
Used In: Part Of:
Ingredient: Dough
Allergens: Gluten
Diet: Vegetarian
Calories: 280
Total Fat: 17g
Protein: 28g
Originated: Italy
Type: Contemporary
Served At: Trattoria
We can use this type of system to
solve complicated data requests
Comparing a graph to a database
how many students in the school?_
Comparing a graph to a database
how many students in the school?_
Identifier
Student 1
Student 2
Student 3
Student 4
First Name
John
Lauren
Paul
Lucy
Last Name
Smith
Jones
Green
King
Comparing a graph to a database
how many students in the school?_
Identifier
Student 1
Student 2
Student 3
Student 4
First Name
John
Lauren
Paul
Lucy
Last Name
Smith
Jones
Green
King
4
Comparing a graph to a database
how many students in the school?_
School
John
Smith
Lauren
Jones
Paul
Green
Lucy King
Type: Student
Gender: Male
School Year: 11
Type: Student
Gender: Female
School Year: 6
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Female
School Year: 10
Attends: Attends:
Attends: Attends:
Comparing a graph to a database
how many students in the school?_
School
John
Smith
Lauren
Jones
Paul
Green
Lucy King
Type: Student
Gender: Male
School Year: 11
Type: Student
Gender: Female
School Year: 6
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Female
School Year: 10
Attends: Attends:
Attends: Attends:
Comparing a graph to a database
how many students in the school?_
School
John
Smith
Lauren
Jones
Paul
Green
Lucy King
Type: Student
Gender: Male
School Year: 11
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Male
School Year: 10
Attends: Attends:
Attends: Attends:
4
Comparing a graph to a database
how many GCSE students are female and take maths?_
Comparing a graph to a database
how many GCSE students are female and take maths?_
Table 1: Course List
Table 2: Course /
School Year
Table 3: School Year /
Study Level
Table 4: Study Level /
Student Name
Table 5: Student Name
/ Gender
Answer…
Comparing a graph to a database
Identifier
Student 1
Student 2
Student 3
Student 4
First Name
John
Lauren
Paul
Lucy
Last Name
Smith
Jones
Green
King
1…?
how many GCSE students are female and take maths?_
Comparing a graph to a database
School
John
Smith
Lauren
Jones
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Female
School Year: 10
Attends:Maths
Studies:
Type: Course
Level: GCSE
Teacher: Mr Jenkins
how many GCSE students are female and take maths?_
etc…
Comparing a graph to a database
School
John
Smith
Lauren
Jones
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Female
School Year: 10
Attends:Maths
Studies:
Type: Course
Level: GCSE
Teacher: Mr Jenkins
how many GCSE students are female and take maths?_
etc…
Comparing a graph to a database
School
John
Smith
Lauren
Jones
Paul
Green
Lucy King
Type: Student
Gender: Male
School Year: 11
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Male
School Year: 6
Type: Student
Gender: Male
School Year: 10
Attends: Attends:
Attends: Attends:
1
how many GCSE students are male and take maths?_
In search, entities are the nodes;
structured data, key attributes
Nodes, attributes and relationships
Football Team:
Season:
Division:
Nickname:
Located In
2019 / 2020
Premier
League
Hammers
London
Fixture Date
Home Team
Away Team
26/12/2019
Crystal
Palace
West Ham
United
West Ham United
Entity
Value pairs (structured data)
who did west ham play on boxing day last year?_
Football Team:
Season:
Division:
Nickname:
Located In
2019 / 2020
Premier
League
Hammers
London
Fixture Date
Home Team
Away Team
26/12/2019
Crystal
Palace
West Ham
United
West Ham United
Nodes, attributes and relationships
who did west ham play on boxing day last year?_
Football Team:
Season: 2019 / 2020
Fixture Date
Home Team
26/12/2019
Crystal
Palace
West Ham United
Division:
Nickname:
Located In
Premier
League
Hammers
London
Home Team
Away Team
Crystal
Palace
West Ham
United
Nodes, attributes and relationships
The connections between entities
help to answer complex questions
London
Airbus 321
A network of connected “things”
London
Airport Name: Gatwick
Operating
Airline:
British Airways
Aircraft Type:
Heathrow
Operating
Airline:
Airbus 321
Airport Name:
A network of connected “things”
London
Airbus 321
A network of connected “things”
London
Airbus 321
London
Airbus 321
A network of connected “things”
London
Airbus 321
London
British Airways
Airbus 321
Heathrow
Gatwick
A network of connected “things”
A network of connected “things”
The web forms the basis of knowledge
that all answer engines can leverage
Using the web as a source of data
Information
On The Web
Knowledge
Graph
Using the web as a source of data
Information
On The Web
Knowledge
Graph
Direct Submission
Using the web as a source of data
Information
On The Web
Knowledge
Graph
Direct Submission
Feeds & Direct Integrations
Using the web as a source of data
Information
On The Web
Knowledge
Graph
Direct Submission
Feeds & Direct Integrations
Trusted Sources
Using the web as a source of data
Information
On The Web
Knowledge
Graph
Direct Submission
Feeds & Direct Integrations
Trusted Sources
Osmosis From The Web
Advances in NLP make it easier to
decode unstructured information
Deciphering meaning is complex
hammers boxing day for sale online_
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
Product category: diy tool
Time period: Boxing Day
Date: 26th December 2019
Transaction medium: online
Transaction event: sale event
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
Left to right understanding
Ignore “common” words
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
*Note: this is a simplified explanation of a far more complex process
Product category: diy tool
Time period: Boxing Day
Date: 26th December 2019
Transaction medium: online
Transaction event: sale event
Deciphering meaning is complex
hammers boxing day for sale online_
Misinterpreted
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
Misinterpreted Not accounted for
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
Left to right understanding
Ignore “common” words
*Note: this is a simplified explanation of a far more complex process
BERT changed this for the better
Deciphering meaning is complex
The Hammers is the nickname of West Ham United football club
BERT – learning through masking words
*Note: this is a simplified explanation of a far more complex process
The Hammers is the nickname of West Ham United football club
The Hammers is the nickname of West Ham United football club
Deciphering meaning is complex
The Hammers is the nickname of West Ham United football club.
BERT - learning through guessing the next sentence
*Note: this is a simplified explanation of a far more complex process
He used them to build a new cabinet.
They used to be based at Upton Park.
Deciphering meaning is complex
hammers boxing day for sale online_
Using BERT to understand each word’s impact
*Note: this is a simplified explanation of a far more complex process
Deciphering meaning is complex
hammers boxing day for sale online_
*Note: this is a simplified explanation of a far more complex process
Product category: tickets
Type: West Ham United
Date: 26th December 2019
Event type: football fixture
Transaction medium: online
The output of this is a system that
can handle more complex “asks”
Queries are a vehicle for discovery
find the best place to eat pizza near me with
veggie options open tonight and takes amex_
find the best place to eat pizza near me with
veggie options open tonight and takes amex_
Intent: Navigation
Feature: For vegetarians Opening: Friday Payment: Card
Rating: 4*+ Cuisine: Italian Location: Paris
Queries are a vehicle for discovery
find the best place to eat pizza near me with
veggie options open tonight and takes amex_
Intent: Navigation
Feature: For vegetarians Opening: Friday Payment: Card
Rating: 4*+ Cuisine: Italian Location: Paris
Time: 8-10pm Card: American ExpressMenu Item: Margherita
Type: Takeaway Distance: <500mContent: ”clean”Type: Location
Queries are a vehicle for discovery
pizza near me
Queries are a vehicle for discovery
pizza near me
Ric Rodriguez
_search history
_demographic
_web actions
_inferred intent
iPhone 11 Pro Max
_ip location
_partner data
_other devices
_routine data
Queries are a vehicle for discovery
Results are ordered to reflect that
specific moment in space and time
Ranking is a multi-layered system
Website 1
Website 2
Website 3
Website 4
Technical
Position 1
Position 2
Position 3
Position 4
Content
Links
Ranking is a multi-layered system
Website 1
Website 2
Website 3
Website 4
“Black box” of
ranking factors
Position 1
Position 2
Position 3
Position 4
Ranking is a multi-layered system
Black box
of “ranking
factors”
Secondary
refinements
Personalisation
Position 1
Position 2
Position 3
Position 4
Website 1
Website 2
Website 3
Website 4
Ranking is a multi-layered system
Black box
of “ranking
factors”
(e.g., speed)
Secondary
refinements
Personalisation
Position 1
Position 2
Position 3
Position 4
Website 1
Website 2
Website 3
Website 4
Ranking is a multi-layered system
Website 1
Website 2
Website 3
Website 4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
Ranking is a multi-layered system
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
#1
#2
#3
#4
Website 1
Website 2
Website 3
Website 4
Constant updates (human and machine)
It’s time to evolve our thinking and
adopt a new framework for search
Information
Search experience marketing
Platform + Presence
Intent
Driven
Queries
Intent
Driven
Answers
Information
Search experience marketing
Accessibility
Platform + Presence
Technical
Information
Search experience marketing
Relevance
Platform + Presence
Content
Information
Search experience marketing
Authority
Platform + Presence
Outreach
There is a paradigm shift happening
in search – embrace it to succeed
Comment ?
- Remplissez le formulaire mis à disposition en
pensant à bien indiquer vos coordonnées
- Assistez au tirage au sort qui aura lieu à la fin du
workshop !
Ric Rodriguez - The Knowledge Graph: A Paradigm Shift In Search (Search Y 2020)

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Ric Rodriguez - The Knowledge Graph: A Paradigm Shift In Search (Search Y 2020)

  • 1. The Knowledge Graph – A Paradigm Shift In Search Ric Rodriguez, SEO Consultant
  • 2. Comment ? - Remplissez le formulaire mis à disposition en pensant à bien indiquer vos coordonnées - Assistez au tirage au sort qui aura lieu à la fin du workshop !
  • 3. 2019 - SEO Consultant, Yext 2018 - SEO Director, iProspect 2016 - SEO Director, Croud 2016 - SEO Strategist, Three Whiskey 2014 - SEO Analyst, iCrossing
  • 4. Technology has enabled people to look for info in different ways
  • 5. Speed to adoption is increasing Time to reach 50m users 46 years 50 years 62 years 64 years 70 0 Sources: Visual Capitalist & Interactive Schools
  • 6. Speed to adoption is increasing Time to reach 50m users 46 years 50 years 62 years 64 years 12 years 22 years 28 years 70 0 Sources: Visual Capitalist & Interactive Schools
  • 7. Speed to adoption is increasing Time to reach 50m users 12 years 7 years 10 months 19 days 22 years 28 years 46 years 50 years 62 years 64 years 70 0 Sources: Visual Capitalist & Interactive Schools
  • 8. The rise of different formats has complicated the landscape too
  • 9. Shifting channels of engagement Time spend by device / day6h 0h 2016201520142012 201720132011201020092008 3h Sources: Kleiner Perkins Mobile Other Desktop
  • 10. Shifting channels of engagement 6h 0h 2016201520142012 201720132011201020092008 3h Sources: Kleiner Perkins Other Desktop Mobile Time spend by device / day
  • 11. Shifting channels of engagement Smart speaker sales (projection)300m 0m 2024202320222020 202520212019201820172016 150m Sources: Loup Ventures / Voicebot.ai 37m units 9m units 73m units52m units
  • 12. Shifting channels of engagement Smart speaker sales (projection)300m 0m 2024202320222020 202520212019201820172016 150m Sources: Loup Ventures / Voicebot.ai 37m units 9m units 73m units52m units 101m units 129m units 172m units 205m units 233m units 291m units
  • 13. People have better access to information than ever before
  • 14. Search has changed over time Source: Live Internet Stats 1998 2018 Discovery # Of Websites In Existence 17m 29m 52m
  • 15. Source: Live Internet Stats 1998 2018 Discovery Prioritisation # Of Websites In Existence 17m 29m 52m 86m 172m 207m 697m Search has changed over time
  • 16. Source: Live Internet Stats 1998 2018 Discovery Prioritisation Answers # Of Websites In Existence 17m 29m 52m 86m 172m 207m 697m 969m 1.04bn 1.6bn Search has changed over time
  • 17. This fundamental change in user behaviour is translating into data
  • 18. Paid Clicks (4.5%) Less people are going to websites Source: SparkToro
  • 19. Organic Clicks (45.5%) Paid Clicks (4.5%) Less people are going to websites Source: SparkToro
  • 20. Zero-Click Searches (50%) Organic Clicks (45.5%) Paid Clicks (4.5%) Less people are going to websites Source: SparkToro
  • 21. Connecting with users at the point of intent is increasingly a challenge
  • 22. Search is more than your website how far is the moon?_
  • 23. how far is the moon?_ Search Answers Websites Meta Search Voice Search Social Media Apps / PWAs Search Results Chat / Message AR / VR Search is more than your website
  • 24. how far is the moon?_ Search Answers Websites Meta Search Voice Search Social Media Apps / PWAs Search Results Chat / Message AR / VR User Interface Search is more than your website
  • 25. how far is the moon?_ Search Answers Websites Meta Search Voice Search Social Media Apps / PWAs Search Results Chat / Message AR / VR Machine Learning User Interface Search is more than your website
  • 26. how far is the moon?_ Search Answers Websites Meta Search Voice Search Social Media Apps / PWAs Search Results Chat / Message AR / VR Machine Learning User Interface Knowledge Graph Search is more than your website
  • 27. how far is the moon?_ Search Answers Websites Meta Search Voice Search Social Media Apps / PWAs Search Results Chat / Message AR / VR Machine Learning User Interface Knowledge Graph Search is more than your website
  • 28. For many, this is a new idea – but it’s how search has worked for a while
  • 29. To organise the world’s information High Crawling
  • 30. To organise the world’s information High Crawling Parsing
  • 31. To organise the world’s information IndexingCrawling Parsing
  • 32. To organise the world’s information High Indexing QueryingCrawling Parsing
  • 33. To organise the world’s information Indexing Querying ServingCrawling Parsing
  • 34. To organise the world’s information ServingCrawling Indexing QueryingParsing
  • 35. To organise the world’s information Indexing Querying ServingCrawling Parsing
  • 36. A knowledge graph enables us to make sense of complicated data
  • 37. An intro to graph (network) theory Y X
  • 38. An intro to graph (network) theory Y X (Although its not unrelated)
  • 39. An intro to graph (network) theory A D B C E Y X
  • 40. An intro to graph (network) theory A D B C E Y X Vertices / Nodes
  • 41. An intro to graph (network) theory A D B C E Y X Edges (connections)
  • 42. An intro to graph (network) theory A D B C E Y X Pizza Pasta Cheese Italian Cuisine Penne
  • 43. However, it’s the relationships and attributes that make the difference
  • 44. Linking data together with meaning Margherita Pizza Italian Cuisine Mozzarella Cheese
  • 45. Linking data together with meaning ObjectSubject Predicate Relationships can “direct” either way
  • 46. Linking data together with meaning Margherita Pizza Mozzarella Cheese Italian Cuisine Used In: Part Of:
  • 47. Linking data together with meaning ObjectSubject Predicate Attributes: Value : Pair Value : Pair Attributes: Value : Pair Value : Pair Each node can have descriptive attributes
  • 48. Linking data together with meaning Margherita Pizza Mozzarella Cheese Italian Cuisine Used In: Part Of: Ingredient: Dough Allergens: Gluten Diet: Vegetarian Calories: 280 Total Fat: 17g Protein: 28g Originated: Italy Type: Contemporary Served At: Trattoria
  • 49. We can use this type of system to solve complicated data requests
  • 50. Comparing a graph to a database how many students in the school?_
  • 51. Comparing a graph to a database how many students in the school?_ Identifier Student 1 Student 2 Student 3 Student 4 First Name John Lauren Paul Lucy Last Name Smith Jones Green King
  • 52. Comparing a graph to a database how many students in the school?_ Identifier Student 1 Student 2 Student 3 Student 4 First Name John Lauren Paul Lucy Last Name Smith Jones Green King 4
  • 53. Comparing a graph to a database how many students in the school?_ School John Smith Lauren Jones Paul Green Lucy King Type: Student Gender: Male School Year: 11 Type: Student Gender: Female School Year: 6 Type: Student Gender: Male School Year: 6 Type: Student Gender: Female School Year: 10 Attends: Attends: Attends: Attends:
  • 54. Comparing a graph to a database how many students in the school?_ School John Smith Lauren Jones Paul Green Lucy King Type: Student Gender: Male School Year: 11 Type: Student Gender: Female School Year: 6 Type: Student Gender: Male School Year: 6 Type: Student Gender: Female School Year: 10 Attends: Attends: Attends: Attends:
  • 55. Comparing a graph to a database how many students in the school?_ School John Smith Lauren Jones Paul Green Lucy King Type: Student Gender: Male School Year: 11 Type: Student Gender: Male School Year: 6 Type: Student Gender: Male School Year: 6 Type: Student Gender: Male School Year: 10 Attends: Attends: Attends: Attends: 4
  • 56. Comparing a graph to a database how many GCSE students are female and take maths?_
  • 57. Comparing a graph to a database how many GCSE students are female and take maths?_ Table 1: Course List Table 2: Course / School Year Table 3: School Year / Study Level Table 4: Study Level / Student Name Table 5: Student Name / Gender Answer…
  • 58. Comparing a graph to a database Identifier Student 1 Student 2 Student 3 Student 4 First Name John Lauren Paul Lucy Last Name Smith Jones Green King 1…? how many GCSE students are female and take maths?_
  • 59. Comparing a graph to a database School John Smith Lauren Jones Type: Student Gender: Male School Year: 6 Type: Student Gender: Female School Year: 10 Attends:Maths Studies: Type: Course Level: GCSE Teacher: Mr Jenkins how many GCSE students are female and take maths?_ etc…
  • 60. Comparing a graph to a database School John Smith Lauren Jones Type: Student Gender: Male School Year: 6 Type: Student Gender: Female School Year: 10 Attends:Maths Studies: Type: Course Level: GCSE Teacher: Mr Jenkins how many GCSE students are female and take maths?_ etc…
  • 61. Comparing a graph to a database School John Smith Lauren Jones Paul Green Lucy King Type: Student Gender: Male School Year: 11 Type: Student Gender: Male School Year: 6 Type: Student Gender: Male School Year: 6 Type: Student Gender: Male School Year: 10 Attends: Attends: Attends: Attends: 1 how many GCSE students are male and take maths?_
  • 62. In search, entities are the nodes; structured data, key attributes
  • 63. Nodes, attributes and relationships Football Team: Season: Division: Nickname: Located In 2019 / 2020 Premier League Hammers London Fixture Date Home Team Away Team 26/12/2019 Crystal Palace West Ham United West Ham United Entity Value pairs (structured data)
  • 64. who did west ham play on boxing day last year?_ Football Team: Season: Division: Nickname: Located In 2019 / 2020 Premier League Hammers London Fixture Date Home Team Away Team 26/12/2019 Crystal Palace West Ham United West Ham United Nodes, attributes and relationships
  • 65. who did west ham play on boxing day last year?_ Football Team: Season: 2019 / 2020 Fixture Date Home Team 26/12/2019 Crystal Palace West Ham United Division: Nickname: Located In Premier League Hammers London Home Team Away Team Crystal Palace West Ham United Nodes, attributes and relationships
  • 66. The connections between entities help to answer complex questions
  • 67. London Airbus 321 A network of connected “things”
  • 68. London Airport Name: Gatwick Operating Airline: British Airways Aircraft Type: Heathrow Operating Airline: Airbus 321 Airport Name: A network of connected “things”
  • 69. London Airbus 321 A network of connected “things”
  • 70. London Airbus 321 London Airbus 321 A network of connected “things”
  • 71. London Airbus 321 London British Airways Airbus 321 Heathrow Gatwick A network of connected “things”
  • 72. A network of connected “things”
  • 73. The web forms the basis of knowledge that all answer engines can leverage
  • 74. Using the web as a source of data Information On The Web Knowledge Graph
  • 75. Using the web as a source of data Information On The Web Knowledge Graph Direct Submission
  • 76. Using the web as a source of data Information On The Web Knowledge Graph Direct Submission Feeds & Direct Integrations
  • 77. Using the web as a source of data Information On The Web Knowledge Graph Direct Submission Feeds & Direct Integrations Trusted Sources
  • 78. Using the web as a source of data Information On The Web Knowledge Graph Direct Submission Feeds & Direct Integrations Trusted Sources Osmosis From The Web
  • 79. Advances in NLP make it easier to decode unstructured information
  • 80. Deciphering meaning is complex hammers boxing day for sale online_ *Note: this is a simplified explanation of a far more complex process
  • 81. Deciphering meaning is complex hammers boxing day for sale online_ Product category: diy tool Time period: Boxing Day Date: 26th December 2019 Transaction medium: online Transaction event: sale event *Note: this is a simplified explanation of a far more complex process
  • 82. Deciphering meaning is complex hammers boxing day for sale online_ Left to right understanding Ignore “common” words *Note: this is a simplified explanation of a far more complex process
  • 83. Deciphering meaning is complex hammers boxing day for sale online_ *Note: this is a simplified explanation of a far more complex process Product category: diy tool Time period: Boxing Day Date: 26th December 2019 Transaction medium: online Transaction event: sale event
  • 84. Deciphering meaning is complex hammers boxing day for sale online_ Misinterpreted *Note: this is a simplified explanation of a far more complex process
  • 85. Deciphering meaning is complex hammers boxing day for sale online_ Misinterpreted Not accounted for *Note: this is a simplified explanation of a far more complex process
  • 86. Deciphering meaning is complex hammers boxing day for sale online_ Left to right understanding Ignore “common” words *Note: this is a simplified explanation of a far more complex process BERT changed this for the better
  • 87. Deciphering meaning is complex The Hammers is the nickname of West Ham United football club BERT – learning through masking words *Note: this is a simplified explanation of a far more complex process The Hammers is the nickname of West Ham United football club The Hammers is the nickname of West Ham United football club
  • 88. Deciphering meaning is complex The Hammers is the nickname of West Ham United football club. BERT - learning through guessing the next sentence *Note: this is a simplified explanation of a far more complex process He used them to build a new cabinet. They used to be based at Upton Park.
  • 89. Deciphering meaning is complex hammers boxing day for sale online_ Using BERT to understand each word’s impact *Note: this is a simplified explanation of a far more complex process
  • 90. Deciphering meaning is complex hammers boxing day for sale online_ *Note: this is a simplified explanation of a far more complex process Product category: tickets Type: West Ham United Date: 26th December 2019 Event type: football fixture Transaction medium: online
  • 91. The output of this is a system that can handle more complex “asks”
  • 92. Queries are a vehicle for discovery find the best place to eat pizza near me with veggie options open tonight and takes amex_
  • 93. find the best place to eat pizza near me with veggie options open tonight and takes amex_ Intent: Navigation Feature: For vegetarians Opening: Friday Payment: Card Rating: 4*+ Cuisine: Italian Location: Paris Queries are a vehicle for discovery
  • 94. find the best place to eat pizza near me with veggie options open tonight and takes amex_ Intent: Navigation Feature: For vegetarians Opening: Friday Payment: Card Rating: 4*+ Cuisine: Italian Location: Paris Time: 8-10pm Card: American ExpressMenu Item: Margherita Type: Takeaway Distance: <500mContent: ”clean”Type: Location Queries are a vehicle for discovery
  • 95. pizza near me Queries are a vehicle for discovery
  • 96. pizza near me Ric Rodriguez _search history _demographic _web actions _inferred intent iPhone 11 Pro Max _ip location _partner data _other devices _routine data Queries are a vehicle for discovery
  • 97. Results are ordered to reflect that specific moment in space and time
  • 98. Ranking is a multi-layered system Website 1 Website 2 Website 3 Website 4 Technical Position 1 Position 2 Position 3 Position 4 Content Links
  • 99. Ranking is a multi-layered system Website 1 Website 2 Website 3 Website 4 “Black box” of ranking factors Position 1 Position 2 Position 3 Position 4
  • 100. Ranking is a multi-layered system Black box of “ranking factors” Secondary refinements Personalisation Position 1 Position 2 Position 3 Position 4 Website 1 Website 2 Website 3 Website 4
  • 101. Ranking is a multi-layered system Black box of “ranking factors” (e.g., speed) Secondary refinements Personalisation Position 1 Position 2 Position 3 Position 4 Website 1 Website 2 Website 3 Website 4
  • 102. Ranking is a multi-layered system Website 1 Website 2 Website 3 Website 4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4
  • 103. Ranking is a multi-layered system #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 #1 #2 #3 #4 Website 1 Website 2 Website 3 Website 4 Constant updates (human and machine)
  • 104. It’s time to evolve our thinking and adopt a new framework for search
  • 105. Information Search experience marketing Platform + Presence Intent Driven Queries Intent Driven Answers
  • 109. There is a paradigm shift happening in search – embrace it to succeed
  • 110. Comment ? - Remplissez le formulaire mis à disposition en pensant à bien indiquer vos coordonnées - Assistez au tirage au sort qui aura lieu à la fin du workshop !