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Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
Using Ontologies to Power AI
SLA Canada July 9th, 2020
WWW.EARLEY.COM
Seth Earley
Earley Information Science
@sethearley
seth@earley.com
www.linkedin.com/in/sethearley
Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
SETH EARLEY - BIOGRAPHY
CEO and Founder
Earley Information
Science
@sethearley
seth@earley.com
www.linkedin.com/in/sethearley
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Speaker
AIIM Master Trainer
Course Developer & Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
Industry conferences on knowledge and information management
Northeastern University
Boston Knowledge Management Forum
Academy of Motion Picture Arts and Sciences, Science and
Technology Council Metadata Project Committee
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data management systems,
taxonomy, ontology and metadata governance strategies
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The AI Powered Enterprise
3
Available now
https://www.amazon.com/AI-Powered-
Enterprise-Ontologies-Business-
Profitable/dp/1928055508/
“A great resource to separate the
hype from the reality and a
practical guide to achieve real
business outcomes using AI
technology.”
—Peter N Johnson, MetLife
Fellow, SVP, MetLife
“I do not know of any books that have
such useful and detailed advice on the
relationship between data and
successful conversational AI
systems.”
—Tom Davenport, President’s
Distinguished Professor at Babson
College, Research Fellow at MIT
Initiative on the Digital Economy, and
author of Only Humans Need Apply
and The AI Advantage
“Read this book to learn how leaders
and companies are using AI with
structured data to transform business.
Insight from real world examples,
combined with a proven methodology,
will arm the reader with the knowledge
and confidence necessary to drive AI
in any organization”.
– Barry Coflan, SVP & Chief
Technology Officer, Schneider Electric
– Digital Energy
Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
Three take aways
1. What the heck is an ontology? Is it the same as master data?
2. Structured and unstructured training data structures need to be consistent,
intentionally managed and reused
3. Using an ontology for reference data can improve efficiency of any
information management programs
4
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Ontology Defined
5
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• The importance of ontology to cognitive applications
• Relationship between taxonomies, thesaurus structures
and ontologies
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Ontologies Describe a Domain of Information
The branch of metaphysics dealing
with the nature of being.
6
A set of concepts and categories in a subject
area or domain that shows their properties
and the relations between them.
Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
“But even those personalities required
proficiency in other facets of the technology
such as an expertly developed domain
model”
“Because intelligent virtual assistants are
focused within a domain model, they benefit
from a clearly defined knowledge base and are
able to go much deeper and stay within those
bounds…”
Source: Analyst Gigaom Research https://gigaom.com/2014/09/01/the-next-step-for-intelligent-virtual-assistants-its-time-to-consolidate/
“…domain models and ontologies are important”
Domain models and ontologies are core to cognitive AI applications
7
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“Sound bite” definitions
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
A Thesaurus is a specialized taxonomy
• Equivalence relationships (synonyms)
• Associative relationships (related terms – “see also”)
• Preferred terms, variant terms
An Ontology is a collection of taxonomies and thesauri
• A body of knowledge is represented by multiple lists of categories
• Categories of various types are conceptually related
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A Continuum of Knowledge Models
9
Controlled
Vocabulary
Thesaurus Taxonomy Ontology
Knowledge
Graph
Modeling can be done at various levels of sophistication and fidelity:
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Taxonomy allows for multiple perspectives
Products
Games
Card games
Action
figures
Board games
Brand
Milton
Bradley
Scrabble
Disney
Battleship
Hierarchical relationships
(parent/child)
Tree-like structure,
categories that branch out
to reveal sub-categories
and terms
Dictionary of preferred
terminology
System for organizing concepts and categorizing content
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Types of Term Relationships
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.
Equivalence Hierarchical Associative
Increasing complexity
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Relationship Types
A
Relationship Examples
E E
A
? ?
H
H
E Equivalence
H Hierarchical
A Associative
Game
Manufacturers
Hasbro, Inc Hasbro
Hasbro Gaming
Hasbro
Industries
Video Games Board Games
?
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Ontology Extends The Set of Relations
Is-a
Product-of
Manufactures
Creation-date
1967
East Longmeadow,
MA
Headquartered-in
Brand
Milton
Bradley
Scrabble
Disney
Battleship
Has-parts
Subtype
Subtype
Products
Games
Card games
Action
figures
Board games
Card games
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Equivalence Terms Associative Terms
• Common misspellings
• Other terms used
• Abbreviations
• Internal names
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• See also
• Related products
• Language spoken
• Products for market
• Available in region
• Risks in region
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A Breadth of Ontologies: It Ain’t Just Data
15
Ontologies can
model multiple aspects of an
enterprise or business
functionality
Information systems
& data
Information sources,
structures, standards, access
credentials, quality and
completeness, query
languages, etc.
Domain knowledge
Tax law, automobile
manufacturing,
pharmaceutics, etc.
The enterprise
Plants, locations, regions,
departments, employees,
products, processes, policies,
KPIs, etc. Clients, vendors,
partners
Customer types and
characteristics, vendor SLAs,
partner capabilities, etc.
Client interactions
Transaction, interaction types,
conversation structure,
channels, etc.
Or, any other concepts that
are important to the
business
Combined, these
knowledge models allow
for more complete question
answering and problem
solving!
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The Value of
Ontologies
“Knowledge scaffolding”
Serves as the organizing principles that can be
overlaid on top of any system or data source to
enable disparate systems to communicate
“Rosetta Stone”
Allows AI algorithms to understand industry and
business specific terminology.
16
Ontologies increase in value as
they are enriched and enhanced
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APPLICATION TO COGNITIVE AND DATA
CENTRIC CHALLENGES
Chatbots, Intelligent
Virtual Assistants
and Question
Answering Systems
17
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Chatbots are a
channel
(… to knowledge, content, data, information…)
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Information Retrieval Continuum
BASIC
SEARCH ENGINE
KNOWLEDGE
PORTAL
VIRTUAL
AGENT
INTELLIGENT
ASSISTANT
KNOWLEDGE
BASE
Any text
Multiple sources
Keyword or full text
query
None necessary, but
Improves with metadata
Search box,
documents list
Search
Multiple sources, separate
taxonomies and schemas
Full text query or
Faceted exploration
Taxonomies, clustering,
classification
Role-Based
Search, classification,
databases
Domain specific ontologies
Highly curated sources
Query, explore facets
Offers related info
Conversational
NLP, search, classification
Process engines
Dynamic info enrichment
improves with interaction
Implicit query /
Recommends based on
users’ history
Conversational, retains
context, personalized
NLP, search, classification
Machine Learning
Ontologies, clustering,
classification, NLP
Ontologies, clustering,
classification, NLP, personalization
SEARCH
INTERACTION
INFORMATION
ARCHITECTURE
USER
EXPERIENCE
ENABLING
TECHNOLOGY
Increasing functionality
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Practical Applications for Ontology
Cleveland Museum – Ontology reference
data for traffic pattern analysis
Allstate – Ontology for semantic
deconstruction
Ontology to drive conversational
commerce
Standardized componentized content for
reuse
Ontology to drive code reuse across
platforms and channels
20
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Cleveland Museum of Art
– visitor pattern analysis
21
CASE STUDY
The Cleveland Museum of Art wanted to understand the
interests of visitors.
Attendance data is broad – the question is where do
they go and what do they look at? What do they like?
In order to do this, they needed to identify the various
characteristics of the collection and define exhibits,
themes, locations and points of interactions.
An ontology was defined to allow for connection of
geospatial data to behavioral analytics as they relate to
specific spaces objects, installations and exhibits in the
museum.
Example Courtesy of Pandata
Traffic Pattern Analysis Correlated with Exhibits and
Collections
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Cleveland Museum
22
Department
Object Type
Location Collection
Example Courtesy of Pandata
Ontology Elements and Terms
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Agent: “I need to determine liability coverages for employee
actions for a collection agency in Massachusetts”
Agent: “Hi, I need some help with a policy”
Allstate - Semantic deconstruction of utterance
Bot: “OK. Can you tell me what kind of policy?”
Topic = “liability coverage”
Product = “employee practices liability”
Nature of business = “collection agency”
Region = “Massachusetts”
Content type = “Guideline”
Entity derivation
Context derivation
Audience = “Certified agent”
Topic
Product
Nature of business
Region
Content type
Audience
Faceted retrieval from
knowledge base
Returns content tagged
with appropriate metadata
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State
Transaction type
Nature of Business
Certification
Topic
Product
Content Type
…
Allstate - Semantic deconstruction of utterance
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Conversational Commerce
25
Semantics, digital assets and metadata as ontology
facets for conversational commerce
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Ontology for Standardized/Normalized/Portable/Reusable Content
26
Standardized
domain specific
schemas for reuse
Field 1
Field 2
Field n
…
Field 1
Field 2
Field 3
Field n
…
ELearning, FAQ’s,
Troubleshooting
charts, support
articles
Componentized
content
Tagging for ingestion
Componentized content can
be repurposed across tools
and technologies Improved CSR
Information Access
Faster time to value for all
information access scenarios
Portability across AI and
Chatbot systems
Improved customer self
service
Metrics aligned with specific
content performance
Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
27
Code (re)use cases
Dynamically drive
chatbot functionality
without changing code
Update dialog and
terminology in business
interface without
changing code
Drive changes to
multiple chatbots by
changing in one place
Point to Financial Services
Ontology =>
Financial Services Chatbot
Point to Insurance Ontology =>
Insurance Chatbot
Change dialog phrasing
Change facets (add new term,
delete term, change term)
Update DialogFlow, Slack, Twilio,
Facebook Messenger, etc.
E.g., adding new offering, region,
content type, industry, etc.
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Financial Services and Insurance Ontologies
28
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Propagating (reusing) dialog and terms
29
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Insurance Experience
30
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Financial Services Experience
31
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Ontology Provides Consistent Architecture at Multiple Levels of Granularity
32
COMMON ENTERPRISE ARCHITECTURE
Context Aware Information Architecture
Content Model Ontology Metadata
Structured
(Operational) Data
Unstructured
(Big) Data
Information Infrastructure
Marketing
Data
User
Data
Product
Data
Historical
Data
Operating
Content
Information Management Platforms
PIM DAM CMS ECM CRM ERP
Customer
Personalization
Content
Publishing
Site
Merchandizing
Product Info.
Management
Digital Commerce
Business
Intelligence
Knowledge
Management
Enterprise Search
Content
Management
Digital
Workplace
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Book Excerpt: Will Your Company Make It Into the AI-
Powered Future?
https://tdwi.org/articles/2020/03/17/adv-all-ai-powered-future.aspx
Ecommerce Times “The Architectural Imperative for
AI-Powered E-Commerce”
https://www.ecommercetimes.com/story/86530.html
Information Week “AI Hot Spots: Where Is Artificial
Intelligence Heading Now?”
https://www.informationweek.com/big-data/ai-machine-learning/ai-
hot-spots-where-is-artificial-intelligence-heading-now/d/d-
id/1337237?page_number=1
Forbes Magazine “Why 'Ontology' Will Be A Big Word
In Your Company's Future”
https://www.forbes.com/sites/cognitiveworld/2018/07/20/why-
ontology-will-be-a-big-word-in-your-companys-future/
Further Reading
“If you're serious about
harnessing the power of AI in your
business — and you should be —
this book will show you how to
make it an operational reality.”
– Scott Brinker, VP Platform
Ecosystem, HubSpot, Editor,
chiefmartec.com
Copyright © 2018 Earley Information Science, Inc. All Rights Reserved.
Seth Earley
CEO
Earley Information Science
Seth@earley.com
781-820-8080
https://www.linkedin.com/in/sethearley
IEEE IT Professional Magazine articles:
“There’s No AI without IA”
“The Problem with AI”
www.earley.com @sethearley

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How Ontologies Power Chatbots

  • 1. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Using Ontologies to Power AI SLA Canada July 9th, 2020 WWW.EARLEY.COM Seth Earley Earley Information Science @sethearley seth@earley.com www.linkedin.com/in/sethearley
  • 2. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. SETH EARLEY - BIOGRAPHY CEO and Founder Earley Information Science @sethearley seth@earley.com www.linkedin.com/in/sethearley Over 20 years experience Current work Co-author Editor Member Former Co-Chair Founder Former adjunct professor Speaker AIIM Master Trainer Course Developer & Master Instructor Data science and technology, content and knowledge management systems, background in sciences (chemistry) Enterprise IA and Semantic Search Information Organization and Access Industry conferences on knowledge and information management Northeastern University Boston Knowledge Management Forum Academy of Motion Picture Arts and Sciences, Science and Technology Council Metadata Project Committee Editorial Journal of Applied Marketing Analytics Data Analytics Department IEEE IT Professional Magazine Practical Knowledge Management from IBM Press Cognitive computing, knowledge and data management systems, taxonomy, ontology and metadata governance strategies
  • 3. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. The AI Powered Enterprise 3 Available now https://www.amazon.com/AI-Powered- Enterprise-Ontologies-Business- Profitable/dp/1928055508/ “A great resource to separate the hype from the reality and a practical guide to achieve real business outcomes using AI technology.” —Peter N Johnson, MetLife Fellow, SVP, MetLife “I do not know of any books that have such useful and detailed advice on the relationship between data and successful conversational AI systems.” —Tom Davenport, President’s Distinguished Professor at Babson College, Research Fellow at MIT Initiative on the Digital Economy, and author of Only Humans Need Apply and The AI Advantage “Read this book to learn how leaders and companies are using AI with structured data to transform business. Insight from real world examples, combined with a proven methodology, will arm the reader with the knowledge and confidence necessary to drive AI in any organization”. – Barry Coflan, SVP & Chief Technology Officer, Schneider Electric – Digital Energy
  • 4. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Three take aways 1. What the heck is an ontology? Is it the same as master data? 2. Structured and unstructured training data structures need to be consistent, intentionally managed and reused 3. Using an ontology for reference data can improve efficiency of any information management programs 4 www.earley.com @sethearley
  • 5. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Ontology Defined 5 www.earley.com @sethearley • The importance of ontology to cognitive applications • Relationship between taxonomies, thesaurus structures and ontologies
  • 6. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Ontologies Describe a Domain of Information The branch of metaphysics dealing with the nature of being. 6 A set of concepts and categories in a subject area or domain that shows their properties and the relations between them.
  • 7. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. “But even those personalities required proficiency in other facets of the technology such as an expertly developed domain model” “Because intelligent virtual assistants are focused within a domain model, they benefit from a clearly defined knowledge base and are able to go much deeper and stay within those bounds…” Source: Analyst Gigaom Research https://gigaom.com/2014/09/01/the-next-step-for-intelligent-virtual-assistants-its-time-to-consolidate/ “…domain models and ontologies are important” Domain models and ontologies are core to cognitive AI applications 7 www.earley.com @sethearley
  • 8. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. “Sound bite” definitions 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 A Thesaurus is a specialized taxonomy • Equivalence relationships (synonyms) • Associative relationships (related terms – “see also”) • Preferred terms, variant terms An Ontology is a collection of taxonomies and thesauri • A body of knowledge is represented by multiple lists of categories • Categories of various types are conceptually related www.earley.com @sethearley
  • 9. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com A Continuum of Knowledge Models 9 Controlled Vocabulary Thesaurus Taxonomy Ontology Knowledge Graph Modeling can be done at various levels of sophistication and fidelity:
  • 10. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com Taxonomy allows for multiple perspectives Products Games Card games Action figures Board games Brand Milton Bradley Scrabble Disney Battleship Hierarchical relationships (parent/child) Tree-like structure, categories that branch out to reveal sub-categories and terms Dictionary of preferred terminology System for organizing concepts and categorizing content
  • 11. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Types of Term Relationships 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. Equivalence Hierarchical Associative Increasing complexity
  • 12. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Relationship Types A Relationship Examples E E A ? ? H H E Equivalence H Hierarchical A Associative Game Manufacturers Hasbro, Inc Hasbro Hasbro Gaming Hasbro Industries Video Games Board Games ?
  • 13. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com Ontology Extends The Set of Relations Is-a Product-of Manufactures Creation-date 1967 East Longmeadow, MA Headquartered-in Brand Milton Bradley Scrabble Disney Battleship Has-parts Subtype Subtype Products Games Card games Action figures Board games Card games
  • 14. Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Equivalence Terms Associative Terms • Common misspellings • Other terms used • Abbreviations • Internal names www.earley.com @sethearley • See also • Related products • Language spoken • Products for market • Available in region • Risks in region
  • 15. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. A Breadth of Ontologies: It Ain’t Just Data 15 Ontologies can model multiple aspects of an enterprise or business functionality Information systems & data Information sources, structures, standards, access credentials, quality and completeness, query languages, etc. Domain knowledge Tax law, automobile manufacturing, pharmaceutics, etc. The enterprise Plants, locations, regions, departments, employees, products, processes, policies, KPIs, etc. Clients, vendors, partners Customer types and characteristics, vendor SLAs, partner capabilities, etc. Client interactions Transaction, interaction types, conversation structure, channels, etc. Or, any other concepts that are important to the business Combined, these knowledge models allow for more complete question answering and problem solving!
  • 16. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com www.earley.com The Value of Ontologies “Knowledge scaffolding” Serves as the organizing principles that can be overlaid on top of any system or data source to enable disparate systems to communicate “Rosetta Stone” Allows AI algorithms to understand industry and business specific terminology. 16 Ontologies increase in value as they are enriched and enhanced
  • 17. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com APPLICATION TO COGNITIVE AND DATA CENTRIC CHALLENGES Chatbots, Intelligent Virtual Assistants and Question Answering Systems 17
  • 18. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com 18 Chatbots are a channel (… to knowledge, content, data, information…)
  • 19. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com Information Retrieval Continuum BASIC SEARCH ENGINE KNOWLEDGE PORTAL VIRTUAL AGENT INTELLIGENT ASSISTANT KNOWLEDGE BASE Any text Multiple sources Keyword or full text query None necessary, but Improves with metadata Search box, documents list Search Multiple sources, separate taxonomies and schemas Full text query or Faceted exploration Taxonomies, clustering, classification Role-Based Search, classification, databases Domain specific ontologies Highly curated sources Query, explore facets Offers related info Conversational NLP, search, classification Process engines Dynamic info enrichment improves with interaction Implicit query / Recommends based on users’ history Conversational, retains context, personalized NLP, search, classification Machine Learning Ontologies, clustering, classification, NLP Ontologies, clustering, classification, NLP, personalization SEARCH INTERACTION INFORMATION ARCHITECTURE USER EXPERIENCE ENABLING TECHNOLOGY Increasing functionality
  • 20. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com Practical Applications for Ontology Cleveland Museum – Ontology reference data for traffic pattern analysis Allstate – Ontology for semantic deconstruction Ontology to drive conversational commerce Standardized componentized content for reuse Ontology to drive code reuse across platforms and channels 20
  • 21. Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. www.earley.com www.earley.com www.earley.com www.earley.com Cleveland Museum of Art – visitor pattern analysis 21 CASE STUDY The Cleveland Museum of Art wanted to understand the interests of visitors. Attendance data is broad – the question is where do they go and what do they look at? What do they like? In order to do this, they needed to identify the various characteristics of the collection and define exhibits, themes, locations and points of interactions. An ontology was defined to allow for connection of geospatial data to behavioral analytics as they relate to specific spaces objects, installations and exhibits in the museum. Example Courtesy of Pandata Traffic Pattern Analysis Correlated with Exhibits and Collections
  • 22. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. Cleveland Museum 22 Department Object Type Location Collection Example Courtesy of Pandata Ontology Elements and Terms
  • 23. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. Agent: “I need to determine liability coverages for employee actions for a collection agency in Massachusetts” Agent: “Hi, I need some help with a policy” Allstate - Semantic deconstruction of utterance Bot: “OK. Can you tell me what kind of policy?” Topic = “liability coverage” Product = “employee practices liability” Nature of business = “collection agency” Region = “Massachusetts” Content type = “Guideline” Entity derivation Context derivation Audience = “Certified agent” Topic Product Nature of business Region Content type Audience Faceted retrieval from knowledge base Returns content tagged with appropriate metadata
  • 24. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. State Transaction type Nature of Business Certification Topic Product Content Type … Allstate - Semantic deconstruction of utterance
  • 25. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. Conversational Commerce 25 Semantics, digital assets and metadata as ontology facets for conversational commerce
  • 26. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. Ontology for Standardized/Normalized/Portable/Reusable Content 26 Standardized domain specific schemas for reuse Field 1 Field 2 Field n … Field 1 Field 2 Field 3 Field n … ELearning, FAQ’s, Troubleshooting charts, support articles Componentized content Tagging for ingestion Componentized content can be repurposed across tools and technologies Improved CSR Information Access Faster time to value for all information access scenarios Portability across AI and Chatbot systems Improved customer self service Metrics aligned with specific content performance
  • 27. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. 27 Code (re)use cases Dynamically drive chatbot functionality without changing code Update dialog and terminology in business interface without changing code Drive changes to multiple chatbots by changing in one place Point to Financial Services Ontology => Financial Services Chatbot Point to Insurance Ontology => Insurance Chatbot Change dialog phrasing Change facets (add new term, delete term, change term) Update DialogFlow, Slack, Twilio, Facebook Messenger, etc. E.g., adding new offering, region, content type, industry, etc.
  • 28. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Financial Services and Insurance Ontologies 28 www.earley.com @sethearley
  • 29. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Propagating (reusing) dialog and terms 29
  • 30. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Insurance Experience 30 www.earley.com @sethearley
  • 31. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Financial Services Experience 31 www.earley.com @sethearley
  • 32. www.earley.com www.earley.com Copyright © 2019 Earley Information Science, Inc. All Rights Reserved. Ontology Provides Consistent Architecture at Multiple Levels of Granularity 32 COMMON ENTERPRISE ARCHITECTURE Context Aware Information Architecture Content Model Ontology Metadata Structured (Operational) Data Unstructured (Big) Data Information Infrastructure Marketing Data User Data Product Data Historical Data Operating Content Information Management Platforms PIM DAM CMS ECM CRM ERP Customer Personalization Content Publishing Site Merchandizing Product Info. Management Digital Commerce Business Intelligence Knowledge Management Enterprise Search Content Management Digital Workplace
  • 33. www.earley.com www.earley.com Copyright © 2020 Earley Information Science, Inc. All Rights Reserved. Book Excerpt: Will Your Company Make It Into the AI- Powered Future? https://tdwi.org/articles/2020/03/17/adv-all-ai-powered-future.aspx Ecommerce Times “The Architectural Imperative for AI-Powered E-Commerce” https://www.ecommercetimes.com/story/86530.html Information Week “AI Hot Spots: Where Is Artificial Intelligence Heading Now?” https://www.informationweek.com/big-data/ai-machine-learning/ai- hot-spots-where-is-artificial-intelligence-heading-now/d/d- id/1337237?page_number=1 Forbes Magazine “Why 'Ontology' Will Be A Big Word In Your Company's Future” https://www.forbes.com/sites/cognitiveworld/2018/07/20/why- ontology-will-be-a-big-word-in-your-companys-future/ Further Reading “If you're serious about harnessing the power of AI in your business — and you should be — this book will show you how to make it an operational reality.” – Scott Brinker, VP Platform Ecosystem, HubSpot, Editor, chiefmartec.com
  • 34. Copyright © 2018 Earley Information Science, Inc. All Rights Reserved. Seth Earley CEO Earley Information Science Seth@earley.com 781-820-8080 https://www.linkedin.com/in/sethearley IEEE IT Professional Magazine articles: “There’s No AI without IA” “The Problem with AI” www.earley.com @sethearley