How Ontologies Power Chatbots

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

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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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

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
• The importance of ontology to cognitive applications
• Relationship between taxonomies, thesaurus structures
and ontologies

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.

“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

“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

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

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

Equivalence Terms Associative Terms
• Common misspellings
• Other terms used
• Abbreviations
• Internal names
• 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

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.

Financial Services and Insurance Ontologies
28

Propagating (reusing) dialog and terms
29

Insurance Experience
30

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

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”

How Ontologies Power Chatbots

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