Understand the key steps to set up your next data discovery initiative for success using the latest methodology and technologies with Earley Information Science. In this webinar we partner with Expert.AI, a recognized leader in document-oriented text analytics platforms to explain the technical and methodological advances that enable better data discovery.

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WEBINAR
WEBINAR
SPEAKERS
Unlock theValue of Data Discovery Using
Knowledge Graphs and HybridAI
SETH EARLEY
FOUNDER & CEO
EIS
THANK YOU
CHRISTOPHE AUBRY
GLOBAL HEAD OF VALUE CREATION
EXPERT.AI
HOST
NAV CHAKRAVARTI
EXECUTIVE CLIENT PARTNER
EIS

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Today’s Speakers Seth Earley
Founder & CEO
Earley Information Science
@sethearley
/in/sethearley/
seth@earley.com
2
Christophe Aubry
Global Head ofValue Creation
Expert.ai.
@expertdotai
/in/christopheaubry/
caubry@expert.ai
Nav Chakravarti
Executive Client Partner
Earley Information Science
in/nav-chakravarti-3b54372/
nav.chakravarti@earley.com

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About Earley Information Science
3
Proven methodologies to organize information and data.
SELL MORE
PRODUCT
SERVICE
CUSTOMERS
EFFICIENTLY
INNOVATE
FASTER
1994
YEAR FOUNDED.
Boston
HEADQUARTERED.
50+
SPECIALISTS & GROWING.

4. About expert.ai
Expert.ai helps teams turn language into
data so they can make better decisions.
PARTNERS
Public Company (EXAI)
350+ employees
Offices across North America and Europe

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BeforeWe Get Started
WE ARE RECORDING SESSIONWILL BE
50 MINUTES PLUS
10 MINUTES FOR
Q&A
YOUR INPUT IS
VALUED
Link to recording & slides
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
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Agenda
• Hybrid AI – Combining Machine Learning and Natural Language
• Information Architecture and Knowledge Graphs
• Real World Examples and Business Impact – Expert.ai
• Sign up for Ask the Experts
• Q&A
6

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Poll
7
1. Haven’t started
2. Exploring
3. Proofs of Concept
4. Small trial deployments
5. Fully operationalized
At what stage are you in your AI investments?

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HybridAI – Combining Machine Learning
and Natural Language techniques
8

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What is HybridAI?
9
The integration of
statistical machine
learning algorithms
with knowledge
represented as facts
and entities.

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AI Requires Contextualization
Machine learning looks for patterns in data.
10
Information architecture in the form of a knowledge graph
is the foundation for “symbolic AI” (the representation of facts and
relationships)
Knowledge provides context for machine learning

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Machine minds? Think like us?
11
https://www.newscientist.com/article/mg25333740-900-hybrid-ai-a-new-way-to-make-machine-minds-that-
really-think-like-us/
Machines don’t
think like us but
can simulate
textual
interpretation.

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Applications for HybridAI
12
Content/Text Analytics allows
• derivation of structure
• identification of patterns
within unstructured content
and text.
• Knowledge extraction
• Mitigation of compliance risks
• Removal of Personally Identifiable Information (PII)
• Identification of patterns of fraud
• Development of Question Answering systems
• Training of IntelligentVirtual Assistants and bots
• Detection of customer sentiment
• Prediction of credit risks
• Feature extraction from product data

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The Importance of Information
Architecture and Knowledge Graphs
13

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InformationArchitecture Process
Effective Knowledge Graphs require a good information
architecture process.
14
Business
Problem
Use Cases
Users &
Impact
Content
Organizing
Principles

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Taxonomies and Ontologies
Taxonomies are building blocks.
Organizations typically have multiple
taxonomies.
Generically:
• Products
• Services
• Processes
• Solutions
If we build relationships
between taxonomies, we are
beginning to build an ontology
• Services for solutions
• Content for products
• Interests of customers
15
• Customers
• Interests
• Content
• Etc.

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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 DistributionTaxonomies
DOCUMENT TYPES
• Installation guides
• Manuals
• Marketing plans
• …
16

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Types ofTaxonomies
Organizing principles vary by industry and organization.
Life sciences:
• Branded drugs
• Generic drugs
• NME’s
• Diseases
• Indications
• Mechanism of action
• Target
• …
Insurance:
• Products
• Services
• Businesses
• Risks
• Regions
• Customer types
• …
Industrial distributor:
• Products
• Industries
• Interests
• Processes
• Environments
• Customer types
• …
17

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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
18

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Data + Ontology = Knowledge Graph
19
1. Begin with taxonomies to
describe the domain
2. Develop relationships between
the taxonomies => ontology
3. Add data to the ontology
structure => knowledge graph
Knowledge
Graphs allow
access to
enterprise
structured and
unstructured
information

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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
• …
20

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Poll
21
Are you currently using taxonomies or
ontologies?
1. Not actively using
2. Evaluating use cases
3. Using for Internal applications
4. Using for External applications
5. Using for Internal and External applications

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Real world examples and business impact
22

23. Unlock the Value of Data
Discovery using Knowledge
Graphs and Hybrid AI
September 28th, 2022
Christophe Aubry
Head of Life Sciences & Media

24. Language is Everywhere
Information
Providers
Life Sciences &
Healthcare
Content Discovery
Editorial Workflow UX
• Medical Notes
• Articles / Patents
• Clinical Trials
• Drug Safety Data
• News Feeds
• Articles
• Recommendations
• Topic Pages
Financial
Services
Mortgage Onboarding
Customer Interaction
• Mortgage Contracts
• Emails
• Online Inquiries
Insurance
Carriers
Underwriting Claims
Risk Management
• Quotes
• Policies
• Claims Notes
• Risk Reports
Medical Notes Analysis
Knowledge Discovery
CI

25. Language is Hard!

26. Language is Hard!

27. Language is Ambiguous
Humans handle most language data
Scaling language-driven processes is difficult
Technology struggles to understand intent in language

28. Choice of AI Technique is
a Key Success Factor
One approach does not fit all needs.

29. Two Types of Techniques
Strengths
• Perceived to require less subject matter
expertise (more scalable)
• Ideal for simple tasks & abundance of
sample data
• Data scientists are expert on this technique
Weaknesses
• Extremely data dependent (suffers data
scarcity & variability)
• High computational costs
• Black box: Lacks explainability, biased
• Improvement can be very time consuming
Strengths
• Less dependent on data
• Lower computational requirements
• White Box: transparent & logical
• Higher accuracy with limited effort
Weaknesses
• Requires specific human expertise & effort
• Curation of domain specific knowledge graph
can be complex
• Data scientists lack of knowledge about this
technique
Machine Learning
Inference-Based
Symbolic AI
Knowledge-Based

30. Hybrid AI
Quick Start
Adaptable
Transparency
Higher Accuracy
Machine
Learning
Symbolic
AI
+
Leverage the strengths of both ML
and knowledge-based techniques to generate results.

31. Data Discovery Applications
AI applied to Language Data

32. What is Data Discovery?
• Actionable Insights to support business decisions
• Produce Business Intelligence Landscapes in real-time to assess
tomorrow’s risk and identify business opportunities
• Build a Strategic Plan to make positive use of the information gathered
• Intelligent Analytics with A.I.
• Analyze news stories, emails, research papers, press releases, financial
reports, or other records for informed decision making
• Automatically connect the dots between Organizations, Key Influencers,
Subject Matters, Dates, Events, Financials or other data

33. Financial
Data
Discovery
A real life example!

34. Financial Data Discovery
Business Challenges
• Data updates restricted to major assets
• Limited number of data points per financial
asset
• Manual process from source crawling to
database creation
How AI Helps
Automate the extraction of key parameters of financial assets.
Bring efficiency to back-office processes and scale data creation.
Collaborative authoring environment to
train NL models
NL Platform to automate key parameters
extraction
UI for seamless validation of key
parameters and database ingestion
The Impact 100+ 50+ users 85%+
Data Quality
Efficient
Data Aggregation &
Validation
Parameters for
Automation
at scale

35. Financial Reporting
1,930
2,910
3Q21

36. Key Financial Information Extraction

37. World Class Knowledge Graph
“One of the highest levels of accuracy in the market”
2 million concepts and entities
6 million relations
12 languages
Different meanings of
‘debt’

38. 38
INTERNAL
Medical
Data
Discovery
National NLP Clinical Challenges (n2c2)
1,200+ Discharge Summaries provided by
Harvard Medical School

39. Medical Data Discovery
Business Challenges
• Limited analytical view on patient journey
within the hospital
• Identify correlations between patient
demographics and diseases
• Fulfill regulatory requirements by
monitoring adverse events
How AI Helps
Automate the extraction of key clinical data points from discharge summaries
Produce analytics dashboards of patient diagnosis, procedures and treatments
• Biomedical knowledge graph for data
standardization
• NL Platform to automate the extraction of
key clinical data and relations
• Seamless integration with an analytics
dashboard

40. Medication Challenge
Entity & Relationship Extraction
0
100
200
300
400
500
1 2 3 4
Patients per Age Category
Disease
Hypertension 734
Infections 665
Edema 581
Hypersensitivity 561
allergy 545
Myocardial Infarction 484
Diabetes 462
Coronary Artery Disease 401
Drug Interactions 385
Ischemia 343
Drug
Lasix 636
aspirin 537
Insulin 523
Potassium 497
Colace 449
Lopressor 421
Lisinopril 338
nitroglycerin 337
atenolol 320
Nexium 292
Sign or Symptom
Chest Pain 640
Fever 622
Dyspnea 608
Pain 433
Nausea 383
Cough 254
Diarrhea 216
Vomiting 210
Wheezing 187
Headache 183
Diagnostic Procedure
Diagnostic Techniques and Procedures 1050
Electrocardiography 736
Plain chest X-ray 445
Auscultation 262
Cardiac Catheterization 257
Tomography, X-Ray Computed 240
Telemetry 121
Echocardiography 98
Palpation 90
Magnetic Resonance Imaging 87
Therapeutic or Preventive Procedure
Therapeutics 723
Coronary Artery Bypass 317
Catheterization 270
Cardiac Catheterization 247
Interventional procedure 246
Physical Therapy Specialty 237
Preventive Medicine 170
Cholecystectomy 137
Appendectomy 111
Percutaneous Transluminal Coronary Angioplasty 99
Data Source: Discharge summaries from n2c2 data set
Data for demonstration purposes
Insights & Analytics Dashboard assists with Patient Health Management

41. Data Source: Discharge summaries from n2c2 data set
Data for demonstration purposes
Medication Challenge
Correlation between Age and Disease
Data Source: Discharge summaries from n2c2 data set
Data for demonstration purposes
0
100
200
300
400
500
1 2 3 4
Patients per Age Category

42. Discharge Summary
HISTORY OF PRESENT ILLNESS
Today, 78-year-old male with the history of Pulmonary Hypertension, Type 2 Diabetes
Mellitus, and Hyperlipidemia presented to ER with worsening chest pain, shortness of
breath, headache and dizziness.
Upon arrival, he was found to be hypoxic with saturation 89% on room air, BP 180/120,
Heart Rate 132.
Chest x-ray revealed Multifocal infiltrates. Labs were performed.
HOSPITAL DISCHARGE PHYSICAL FINDINGS
1. COVID-19 Infection
2. Diabetic ketoacidosis caused by Diabetes
3. Chronic obstructive pulmonary disease (COPD) due to pulmonary hypertension
4. Acute respiratory acidosis caused by diabetic ketoacidosis
HOSPITAL DISCHARGE FOLLOWUP
1. Remdesivir 200mg IV for treatment of COVID-19
2. Tiotropium 5mcg (2 inhalations) orally once a day for treatment of COPD
3. Riociguat for Pulmonary Hypertension (PAH)

43. Medication Challenge
Entity & Relationship Extraction
60-year-old male with history of hypertension, diabetes,
dyslipidemia, who presented to the ER on 9/3/2020 with
worsening chest pain and shortness of breath. Upon arrival, he
was found to be hypoxic with saturations 84% on room air,
hypotensive 60/40 and febrile. Chest x-ray revealed multifocal
infiltrates. Labs revealed acute kidney injury.
Gather insights from Discharge Summaries

44. Medication Challenge
Entity & Relationship Extraction
60-year-old male with history of hypertension, diabetes,
dyslipidemia, who presented to the ER on 9/3/2020 with
worsening chest pain and shortness of breath. Upon arrival, he
was found to be hypoxic with saturations 84% on room air,
hypotensive 60/40 and febrile. Chest x-ray revealed multifocal
infiltrates. Labs revealed acute kidney injury.
Convert Unstructured data to Structured Data

45. Medication Challenge
Entity & Relationship Extraction
Understand relevant coding

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Getting Started
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Limited Opportunity
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Answer your questions about
Information Design, Hybrid AI,
Data Discovery,Text Analytics
and related areas.
AskThe Experts
Sign up today!
• 20-minute session
• No cost
• Slots available over the
next 2 weeks
Nav Chakravarti,
Executive Client
Partner, EIS
Christopher Aubry,
Global Head ofValue
Creation, Expert.ai
• Business value
• Common use cases
• Key problems solved
• Typical results
• Sample timeframes

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Ask the Experts - Sign UpToday
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Click on the
link in the chat!

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Q&A
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Resources
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From Expert.ai
Unlocking-the-Value-of-AI-in-Life-Sciences
https://www.expert.ai/wp-
content/uploads/2021/06/Unlocking-the-Value-
of-AI-in-Life-Sciences.pdf
ElevateYour Information Services
Business with AI
https://www.expert.ai/wp-
content/uploads/2021/07/info-services-wp_Sept-
2021.pdf
From Earley Information Science
Knowledge Graphs, aTool to Support
Successful DigitalTransformation
Programs
https://www.earley.com/insights/knowledge-
graphs-a-tool-to-support-successful-digital-
transformation-programs
Knowledge is Power: Context-Driven
DigitalTransformation
https://www.earley.com/insights/knowledge-
power-context-driven-digital-transformation

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EarleyAI Podcast
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Listen to the Earley AI Podcast to explore what's
emerging in technology, data science, and
enterprise applications for artificial intelligence and
machine learning and how to get from early-stage
AI projects to fully mature applications.
Found wherever you listen to podcasts, including…
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Chief Digital Officer,
Evonik
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Chief Customer Officer,
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RECENT EPISODES

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CONTACT US
CONTACT US
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Thank you for your time.We’d love to hear from you!
For
Earley Information Science
www.earley.com
Seth Earley
Seth@earley.com
Nav Chakravarti
nav.chakravarti@earley.com
For
Expert.ai
https://www.expert.ai
Christophe Aubry
caubry@expert.ai

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Thanks!
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