Webinar: Powering Personalized Search with Knowledge Graphs

Copyright © 2022 Earley Information Science, Inc. All Rights Reserved.
www.earley.com www.oxfordsemantic.tech
WEBINAR
WEBINAR
SPEAKERS
Powering Personalized Search with
Knowledge Graphs
SETH EARLEY
FOUNDER & CEO
EIS
DAVE SKROBELA
MANAGING DIRECTOR
EIS
THANK YOU
PETER CROCKER
CEO & CO-FOUNDER
OXFORD SEMANTIC TECHNOLOGIES

Today’s Speakers
SETH EARLEY
Founder & CEO
Earley Information
Science
@sethearley
https://www.linkedin.com/in/sethearley/
Seth@earley.com
DAVE SKROBELA
Managing Director
Earley Information
Science
@daveskrobela
https://www.linkedin.com/in/skrob
ela/
Dave.Skrobela@earley.com
2
PETER CROCKER
CEO & Co-Founder
@oxfordsemantic
https://www.linkedin.com/in/peter-
crocker/
peter.crocker@oxfordsemantic.tech

Before We Get Started
WE ARE RECORDING SESSION WILL BE
50 MINUTES PLUS
10 MINUTES FOR
Q&A
YOUR INPUT IS
VALUED
Link to recording will be
sent by email after the
webinar
Use the Q&A box to
submit questions
Participate in the polls
during the webinar
Feedback survey
afterward (~1.5 minutes)
Thank you to our media partners : CMSWire & Marketing AI Institute
3

Agenda
• Introduction to personalization and the information
framework needed to support it.
• How knowledge graphs are applied to improve the search
experience.
4

Poll
5
1. Getting started
2. Limited PoC’s
3. Deployed in siloed
applications
4. Operationalized
throughout the customer
journey
Where are you on your personalization efforts?

Introduction to the Information Framework
Needed to Support Personalization
6

Promise of Personalization
7
TO GET IT RIGHT, ORGANIZATIONS NEED TO:
• Understand the customer and their needs in detail
• Monitor their real time and near real time digital body
language
• Respond with appropriate content, information and
products in a way that aligns with the customer’s mental
model
• Configure the digital machinery to orchestrate the
experience at scale
• Have clean, consistent, complete and harmonized data
across channels and touchpoints
PERSONALIZATION
HAS BEEN THE BIG
PROMISE FOR THE
PAST 15 YEARS.
THE PROBLEM IS
THAT THIS VISION
IS STILL A LONG
WAY FROM
REALITY.

PERSONALIZATION = RECOMMENDATION
“This is what I think you need based
on what I know about you and
people who are similar to you.”
8

www.earley.com www.oxfordsemantic.tech 9
Search is a Recommendation
Search processes a signal
(the search query) and
responds with a
recommended result – a
prediction based on not only
the search phrase, but
weighted with other factors
* “…search terms are
short, ambiguous and an
approximation of the
searcher’s real
information need…”
* Source: https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/WhiteCONTEXT2002.pdf
Ryen W. White, Joemon M. Jose and Ian Ruthven

Search is a Conversation
After a query is processed, the results are
typically presented with refiners and filters.
Those filters are a response that “asks for”
additional clarification
Imagine walking up to a counter in a hardware
store and saying “tools”.
The clerk might ask “what kind of tools are you
interested in?”

“Tools
”
“What kind of tools?”

Other Signals Can Inform the
Conversation
If a person is wearing paint-stained coveralls and has a hat labeled
“Joe’s painting” a salesperson might infer that they are a painter.
If they ask for “supplies” one can reasonably infer
that they want painting supplies and materials
The signals about that person informs the next stage
of the conversation.
In this case, the appearance of the customer provides physical
cues – appearance is part of physical body language.

“What kind of supplies?”
Based on your
appearance, my guess is
that you need painting
supplies.
“Supplies”

Digital Body Language
14
In the online
world, digital body
language is
represented by
metadata.
We capture the
details of our
target in a
Customer Identity
Graph.
Every tool, app, site
and touchpoint
produces data
about the user.

Digital Body Language = Metadata
The customer’s digital body language is
represented by metadata.
Metadata become signals that search engines
can leverage to personalize results

Search is about METADATA
(even when there is none).
Search algorithms use metadata to
retrieve information

Poll
17
1. Not on the radar
2. Initial investigation
3. Limited PoC’s
4. Deployed in multiple applications
Have you deployed Knowledge Graph
technology?

Ontology is the formal
knowledge scaffolding of
the enterprise
Graph data fills in that
scaffolding with the
operational and
transactional data of the
organization

Taxonomy enables information access from
multiple perspectives
• System for organizing concepts
and categorizing content
• Hierarchical relationships
(parent/child)
• Tree-like structure, categories that
branch out to reveal sub-categories
and terms
• Dictionary of preferred terminology
• Key observation: taxonomy is not
the same as navigation.
Products
Games
Card games
Action
figures
Board games
Brand
Milton
Bradley
Scrabble
Disney
Battleship
Internal perspectives may be different from
what is important to external audiences.

Types of Term Relationships
Increasing complexity
From source data
Inferred or computed
Equivalence Hierarchical Associative
• Used in thesauri.
• Also called
“entry types” of
terms.
• Synonyms.
• Things that are related
conceptually.
• Associative relation types
are context and audience
specific.
• This is how we might
relate multiple taxonomies.
Purist definition of
a taxonomy –
terms have parent/child
relationship.
20

A TAXONOMY IS A LIST OF TERMS THAT
ENABLE CLASSIFICATION OF INFORMATION
Method used to organize Subject/Topic
metadata
Typically expresses hierarchical relationships
(parent/child)
Emphasizes context
“Sound bite” definitions
AN ONTOLOGY IS A COLLECTION OF
RELATED TAXONOMIES AND THESAURI
A body of knowledge is represented by
multiple lists of categories
Categories of various types are conceptually
related
Typically uses a full range of logical
expressions (not just parent/child) to show
relationship
A THESAURUS IS A SPECIALIZED TAXONOMY
Equivalence relationships (synonyms)
Associative relationships (related terms – “see
also”)
Preferred terms, variant terms
SEMANTIC REASONING
Includes Ontology based inferencing
Extends with more advanced reasoning:
mathematical, logical, aggregations,
negation …
21

DEPARTMENTS
INDUSTRIAL DIST
• Fastenal
• Grainger
• MSC
• Wolseley
• …
ENVIRONMENTS
• Marine
• Underground
• Confined Space
• …
PROCESSES
• Rough Cut
• Finish Cut
• Polishing
• Coating
• ...
TASKS
• Extraction
• Fabrication
• Joining
• Separating
• …
PRODUCTS
• Abrasives
• Clamping
• Fasteners
• …
INDUSTRIES
• Mining
• Food Processing
• Healthcare
• …
CUSTOMERS
• Hitachi
• Schlumberger
• Toyota
• …
INTERESTS
• Prototyping
• MRO
• Replenishment
• …
• Tech Support
• Merchandising
• Sales
• …
ROLE
• Design engineer
• Maintenance
engineer
• Procurement Mgr
• ...
Industrial Distribution Taxonomies
DOCUMENT TYPES
• Installation guides
• Manuals
• Marketing plans
• …
22

Industrial Distribution Ontology
Industrial
Distributors
Departments
Industries
Interests
Role Customers
Products
• Tech Support
• Merchandising
• Sales
• …
• Abrasives
• Clamping
• Fasteners
• …
• Marine
• Underground
• Confined Space
• …
• Equipment
maintenance
• Repair
• Finishing
• ...
• Extraction
• Fabrication
• Joining
• Separating
• …
• Manufacturing
• Mining
• Food Processing
• Healthcare
• …
• Prototyping
• MRO
• Replenishment
• …
Environments
Tasks
Document
Types
ABCo
Competitors
ABC Company
H
H
A
A
A
A
A
A
A
A
H
E
• Fastenal
• Grainger
• MSC
• Wolseley
• …
• Hitachi
• Schlumberger
• Toyota
• …
• Installation guides
• Manuals
• Marketing plans
• …
Processes
H
A
• Procurement
• Maintenance Engineer
• …
A
23

Industrial Distribution Knowledge Graph
Customers
Tasks
ABC
Company
Document
Types
Industries
Products
Processes
Roles
Used for
Interests
• Prototyping
• MRO
• Replenishment
• …
• Manufacturing
• Mining
• Food Processing
• Healthcare
• …
• Extraction
• Fabrication
• Joining
• Separating
• …
• Equipment Maintenance
• Repair
• Finishing
• ...
• Procurement
• Maintenance Engineer
• …
24

Poll
25
1. Not yet
2. Initial investigation
3. Limited PoC’s
4. Deployed and operationalized
Have you used Knowledge
Graph technology to improve
search?

Personalization via KGraph
What kind of mold?
Injection mold?
Mold & mildew?
26

Personalization via Knowledge Graph
A knowledge graph can be used
to provide user context.
Customer profile data provides
the following clues:
Industry: Manufacturing
Role: Maintenance Engineer
Interests: MRO
Processes: Fabrication
Tasks: Equipment maintenance
These signals inform
recommendation and
enable personalization
This user is interested in
maintaining an injection
mold. 27

Customer Identity Knowledge Graph
Customer
Industries
Products
As built
installation
Roles
Interests
Service history
Demographics
Campaign
responses
Account
master data
28

The User’s “Digital Body Language”
How do we
describe
context?
With metadata.
29

Explicit and Implicit Customer Metadata
Where do we get
metadata?
By collecting
signals
instrumented
throughout the
user journey
30

Using a “High Fidelity” Journey Map
Understand the customer journey
Identify details of the customer
Define content needed White Paper Product compare
tool
Installation
guide
Static Customer Data Dynamic Customer Data
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE

high-fidelity customer journey model
customer model
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
INTELLIGENT
PERSONALIZATION
Component content model
User journey/customer model
Product data model
Knowledge architecture
Static metadata:
(industry, role, interests,
firmographics, etc.)
Customer Data
Platform
Action = Download white
paper
Action = Product
compare, purchase
Action = Download
installation guide
Action = Open offer
email, click through to
site, click offer
Dynamic customer model
Dynamic metadata:
campaign responses, click
through, recent purchases,
new goals change customer
metadata model, and
therefore audience
descriptors real time
Delivering Personalized Customer Experiences – At
Scale
What does it take to do this right? Dynamic metadata identifies changing, real time. signals
about customer goals and intent while they go through
their journey
32

customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
INTELLIGENT
PERSONALIZATION
Product data model
Static metadata:
Dynamic metadata:
metadata model, and
therefore audience
Customer Data
Platform
Top of funnel content
(background on the issues
and challenges)
Content type = White Paper
Topic = Predictive maintenance
Industry = Manufacturing
Stage = Awareness
Role = Technical
Product = Basic Widget
Product
Offer = New customer
paper
1
Delivering Personalized Customer Experiences – At Scale
their journey
33

customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
INTELLIGENT
PERSONALIZATION
Product data model
Static metadata:
Dynamic metadata:
metadata model, and
therefore audience
Customer Data
Platform
Product
Middle of funnel content
(product selector,
comparisons)
Content type = Product
compare tool
Topic = How to choose
Stage = Shop
Role = Technical
Product = Deluxe Widget
paper
Action = Product
compare, purchase
2
What does it take to do this right?
Scale
www.linkedin.com/in/sethearley
and challenges)
Stage = Awareness
Role = Technical
Dynamic metadata identifies changing, real time. signals
their journey
34

customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
INTELLIGENT
PERSONALIZATION
Product data model
Static metadata:
Dynamic metadata:
metadata model, and
therefore audience
Customer Data
Platform
Product
(product selector,
comparisons)
compare tool
Topic = How to decide
Stage = Shop
Role = Technical
Post purchase support
content (install guides,
troubleshooting info)
Content type = Installation
guide
paper
Action = Product
compare, purchase
Action = Download
installation guide
3
Scale
and challenges)
Stage = Awareness
Role = Technical
their journey
35

customer model
CMS and PIM
I RENEW
I INSTALL & I USE
I SHOP & I BUY
I’M AWARE
Content
INTELLIGENT
PERSONALIZATION
Product data model
Static metadata:
Dynamic metadata:
metadata model, and
therefore audience
Customer Data
Platform
Product
(product selector,
comparisons)
compare tool
Topic = How to decide
Stage = Shop
Role = Technical
Post purchase support
content (install guides,
troubleshooting info)
Content type = Installation
guide
Product = New and
Improved Super Widget
Post purchase nurture
content (how to get the
most from your Deluxe
Widget)
Content type = User tips
Product = Deluxe widget
Content type = Promo
Product = Super Deluxe
widget
Offer = Existing customer
paper
Action = Product
compare, purchase
Action = Download
installation guide
Action = Open offer
email, click through to
site, click offer
4
Scale
and challenges)
Stage = Awareness
Role = Technical
their journey
36

PRODUCT ARCHITECTURE
COMPONENTIZED CONTENT
CUSTOMER DATA FOUNDATION
What does it take to do this right?
advanced services & expertise
unified models standardized platforms & processes
• enriched customer journeys
• product attribute model &
corresponding taxonomies
• data intake, clean-up, aggregation.
• analysis, recommendation & decision
making
(ML, data science, human judgment)
• process setup (continuous or periodic)
• standard pipeline for insight
delivery to marketing teams
KNOWLEDGE & INSIGHTS
• product data with e-catalog and
display hierarchies optimized for
customer journeys
• back-end product information
onboarding process aligned with
customer experience practices
• metrics driven decision making
• merchandizer collaboration with product
and solution experts
• configure price quote and
recommendation tools aligned with user
personas and pain points
• product information
management ecosystem aligned
with rich media
• cross sell and upsell relationships
• merchandizing and solution
bundles
• optimized content structure
• component architecture aligned with
messaging architecture
• content attribute model &
corresponding taxonomies
• omnichannel offer recommender
• dynamic offer generator
• content assembly based on offering
architecture and baseline hypotheses
tested against target outcomes
• recombination tested continuously using
changing messaging architecture
• component content
management system
• content production workflows
• content standards & governance
• high fidelity customer journeys with
augmentation and automation
opportunities
• customer attribute model &
corresponding multi-dimensional
audience taxonomies
• profile standardization
• pattern recognition
• customer signal reconciliation across
upstream platforms
• machine learning development &
training
• customer data platform
• customer data modeling
• cross system normalization
• metrics aligned data governance
decision making
Scale
37

Ontology Encodes Graph Data To Provide Consistent Architecture
COMMON ENTERPRISEARCHITECTURE
Context-Aware Information Architecture
ContentModel Ontology Metadata
More structured
(Operational) Data
Less structured (Big)
Data
Information Infrastructure
Marketing
Data
User
Data
Product
Data
Historical
Data
Operating
Content
Information Management Platforms
PIM DAM CMS ECM CRM ERP
CustomerPersonalization
Content
Publishing
Site
Merchandizing
ProductInfo. Management
Digital Commerce
BusinessIntelligence
Knowledge
Management
EnterpriseSearch
ContentManagement
Digital Workplace

Applying Knowledge Graphs to Search
39

www.earley.com www.oxfordsemantic.tech © Oxford Semantic Technologies 2022 40
Applying this to Search
Step 1: Identifying candidate results
• The user’s search provides the input
• The product catalogue organised by
taxonomy is queried
• The taxonomy is used to expand the list
of possible results
• But at this stage there is no ordering…
Product
Data
Search
against the
Knowledge
Graph
Product
catalogue
organized by
taxonomy

www.earley.com www.oxfordsemantic.tech © Oxford Semantic Technologies 2022
Step 2: Product category ranking
• The user’s “Digital Body Language” is brought into play
• Their context is compared to others with similar “body language” assigning a persona
• Each persona holds a category ranking, again organised under the product taxonomy
User
Data
Category Ranking Product
Data
Search
against the
Knowledge
Graph
Persona to
product
taxonomy
ranking
Product
catalogue
organized by
taxonomy
41

www.earley.com www.oxfordsemantic.tech © Oxford Semantic Technologies 2022 42
Step 3: Presenting the results
• The results are ordered using the
persona category ranking
• The top category and results are
presented
User
Data
Category Ranking
Persona to
product
taxonomy
ranking
Search
against the
Knowledge
Graph
Product
Data
Product
catalogue
organized by
taxonomy

User
Data
Category Ranking
Persona to
product
taxonomy
ranking
Search
against the
Knowledge
Graph
Facets to
refine the
results
Product
Data
Product
catalogue
organized by
taxonomy
Step 4: Expand with facets
• Retrieve relevant facets from
the knowledge graph
• Users can now use faceted
search to refine their results
43

User
Data
Category Ranking
Persona to
product
taxonomy
ranking
Search
against the
Knowledge
Graph
Facets to
refine the
results
Product
Data
Product
catalogue
organized by
taxonomy
44

Configuration Management
Computing compatibility
An improved electronic sizing tool created for a global manufacturer
• Thousands of components
• Many millions of configurations
• A few hundred rules determine relevant results at search time
• Domain experts input their knowledge to the system, forming
rules that dictate configurations
• Time to configure, price, and quote reduced
from minutes to less than a second
• Updates in seconds rather than hours
• Using rules to improve queries gives
performance and flexibility unobtainable
with standard CPQ engines
Configuring Automation Solutions with Knowledge Graphs
https://uploads-ssl.webflow.com/5ed7f18d11a068aa460ce2e9/5f5252796dd12f613510c1eb_Festo%20Case%20Study.pdf
45

• Use personal data to enhance recommendations
• Entirely on-device for security
• Integration of multiple data sources
Recommendations such as:
• Retail and ecommerce products bought by people
like you (Amazon)
• Movies that people with similar interests enjoy
(Netflix)
• News articles related to your interests and latest
reads (Google)
• Songs and artists popular in your travel destination
that align with your tastes (Spotify)
RDFox Applications
Improving smartphone recommendations with data security
Photo by Rami Al-zayat on Unsplash
On Device Reasoning Based Context Aware Recommendation
System
https://uploads-
ssl.webflow.com/5ed7f18d11a068aa460ce2e9/5fbe349cb0b0b4a4da914a29_On_device_reas
oning_based_context_aware_recommendation_system_to_preserve_privacy.pdf
46

Getting Started
• Begin by documenting user journeys
• Identify how technology at each touchpoint represents the customer (the attribute model)
• Capture customer metadata models in a knowledge graph
• Segment audiences based on a small number of attributes: target industries, buying roles,
interests
• Rank categories based on relevance to identified segments
• While tedious, this process seeds an orchestration algorithm that can further refine weightings
and begin to identify finer grained product/segment relationships
• Start simply with only a few ranking elements
• Performance baselines and ongoing measurement identifies what works and what does not
• Continue to experiment and refine
47

Thanks!
48

Resources
49
Try RDFox for yourself:
https://www.oxfordsemantic.tech/tryrdfoxforfr
ee
More on Knowledge Graphs and Reasoning
from OST
https://www.oxfordsemantic.tech/blog
EIS Insights - Knowledge Graphs: A Tool To
Support Successful Digital Transformation
Programs
https://www.earley.com/insights/knowledge-
graphs-a-tool-to-support-successful-digital-
transformation-programs
Ontology: The Key to Unlocking the Power
of AI
https://www.earley.com/insights/ontology-
key-unlocking-power-ai

CONTACT US
CONTACT US
50
Thank you for your time. We’d love to hear from you!
For
Earley Information Science
www.earley.com
Seth Earley
Seth@earley.com
Dave Skrobela
Dave.Skrobela@earley.co
m
For
Oxford Semantic Technologies
https://www.oxfordsemantic.tech/
Peter Crocker
Peter.Crocker@oxfordsemantic.te
ch

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