Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.

1. IA FAST TRACK TO
SEARCH-FOCUSED AI SOLUTIONS
Exploring Taxonomy, Ontology, and Knowledge Graphs
and the Fast Track to Artificial Intelligence Strategies
Tatiana Baquero Cakici & Sara Mae O’Brien-Scott
IAC24 - April 11, 2024

2. Agenda
Federal
What are Enterprise AI
and search-focused
solutions?
1
2
Why are they
important?
3 Real world AI use case
4
What do you need to get
there?

3. Tatiana Baquero
Cakici
(she/her)
Senior KM Consultant and
Taxonomist
Sara Mae
O’Brien-Scott
(she/her)
Senior Semantic Engineering
Consultant
Presenters

4. ENTERPRISE KNOWLEDGE
Meet Enterprise Knowledge
10 AREAS OF EXPERTISE
● KM STRATEGY & DESIGN
● TAXONOMY & ONTOLOGY
DESIGN
● AGILE, DESIGN THINKING &
FACILITATION
● CONTENT & DATA STRATEGY
● KNOWLEDGE GRAPHS, DATA
MODELING, & AI
Clients in 25+ Countries Across Multiple Industries
HEADQUARTERED IN
ARLINGTON, VIRGINIA,
USA
GLOBAL OFFICE IN
BRUSSELS, BELGIUM
● ENTERPRISE SEARCH
● INTEGRATED CHANGE
MANAGEMENT
● ENTERPRISE LEARNING
● CONTENT AND DATA
MANAGEMENT
● ENTERPRISE AI
80+
EXPERT
CONSULTANTS
AWARD-WINNING
CONSULTANCY
KMWORLD’S
● 100 COMPANIES THAT MATTER IN KM (2015-2024)
● TOP 50 TRAILBLAZERS IN AI (2020-2023)
INC MAGAZINE
● THE 5000 FASTEST GROWING COMPANIES (2018-2022)
● BEST WORKPLACES (2018-2019, 2021-2023)
WASHINGTONIAN MAGAZINE’S
● TOP 50 GREAT PLACES TO WORK (2017)
WASHINGTON BUSINESS JOURNAL’S
● BEST PLACES TO WORK (2017-2020)
ARLINGTON ECONOMIC DEVELOPMENT’S
● FAST FOUR AWARD – FASTEST GROWING COMPANY (2016)
VIRGINIA CHAMBER OF COMMERCE’S
● FANTASTIC 50 AWARD – FASTEST GROWING COMPANY (2019, 2020)
Top Implementer of
Leading Knowledge and
Data Management Tools
500+ Thought Leadership
Pieces Published

5. WHAT IS
ENTERPRISE AI?

6. What is
Enterprise AI
(e-AI)?
ENTERPRISE AI ENTAILS LEVERAGING MACHINE
CAPABILITIES TO DISCOVER AND DELIVER
ORGANIZATIONAL KNOWLEDGE, DATA AND
INFORMATION IN A WAY THAT CLOSELY ALIGNS WITH
HOW WE LOOK FOR AND PROCESS INFORMATION.
@EKCONSULTING

7. Enterprise AI Categories
Check out the full article here
@EKCONSULTING

8. Enterprise AI and Semantic Layer
Value of the
Semantic Layer:
➔ Enterprise
Standardization
➔ Interoperability
➔ Reusability
➔ Explainability
➔ Scalability
A Semantic Layer:
● Makes data available for both humans and machines to understand;
● Captures and connects content and data based on business or domain meaning and value;
● Aggregates and unifies unstructured and structured data to connect data of all formats; and
● Enables data federation and virtualization. @EKCONSULTING

9. AI Search Solutions and IA Applications
NATURAL LANGUAGE
SEARCH
CATEGORIZATION AND
CLASSIFICATION TOOL
ADVANCED ANALYTICS
RECOMMENDATION
SYSTEM
• Model information how the
user would describe the
information in speech.
• Develop human-centered
applications using simple
natural language, such as
chatbots.
• Organize content and data
through the right channel(s)
to enable findability and
discoverability.
• Scale data governance to
remove error-prone manual
burden from human
categorization.
• Mine information, discover
hidden facts, and identify
patterns at a large scale.
• Make relevant and timely
decisions, as well as forecast
or predict future outcomes.
• Define relationships
between information to add
context and recommend to
users.
• Recognize patterns that
enable users to discover new
knowledge.
How many projects
did we do with our
partner organization
in New York in 2018?
@EKCONSULTING

10. Different kinds of algorithms and
machine learning models leveraging
semantics are used to generate
personalized recommendations for the
various parts (rows) of the Netflix
homepage.
At Airbnb, data standardization
and machine learning algorithms
work together to consider a
listing’s quality, popularity, price,
and location to determine how it
appears in search results.
Enterprise AI Search Solutions We Know
@EKCONSULTING

11. Additional Enterprise AI Search Solutions
Expert Locator
Recommendation Engine
Data Management
Advanced Analytics & Automated Reporting
@EKCONSULTING

12. WHY IS
ENTERPRISE AI
IMPORTANT?

13. Benefits of a Knowledge Graph & AI for Search
Understanding Context
Semantic standardization and
relationships between information
gives us a better understanding of
how things fit together so that
search can be more precise.
Natural Language Search
Graphs store information the way
people speak. Integrating a graph
into your search makes natural
language search easier to
implement.
Audience Targeting
AI-powered search engines can
analyze user preferences and
behaviour to deliver more relevant
search results.
Scalable Data and Content
Integration
Graphs allow for the integration of
structured and unstructured
information so that users can
search for data and content at the
same time.
Aggregation
Graphs allow for aggregation of
information from multiple
disparate solutions so that search
results can display information that
exists in multiple locations and
formats.
Real-time Analytics
Graphs allow organizations to
improve the user experience and
content relevance based on
real-time analytics on search
queries and content interactions.
@EKCONSULTING

14. REAL WORLD
SEARCH-FOCUSED
AI USE CASE

15. Personalized Course Recommender
Assessment
Questions
Courses
What is the
Question about?
What is the
Course about?
Which courses should be recommended to the user?
How are the Concepts
relevant to each other?
Healthcare
Professional
A healthcare workforce solutions provider wanted to increase engagement and learning outcomes by
delivering personalized content via their learning platform.
@EKCONSULTING

16. Taxonomy & Ontology for
Recommender
How to Treat
Women with
Diabetes During
Pregnancy
Condition
Condition
Condition
Condition
Demographic
Demographic
Gestational
diabetes may
result in an
increased
maternal risk of
developing
type 2
diabetes.
Gestational
Diabetes
Type 2
Diabetes
Maternal
Pregnancy
Diabetes
Women
Course Name
Course
Description
Course Outline
Learning
Objective
Question
Course
● Relationships are
depicted with
arrows.
● You can see an
example of a single
path connecting the
Question to Course
with purple
arrows.
@EKCONSULTING

17. WHAT DO YOU
NEED TO GET
THERE?

18. BASIC
SEARCH
ENTERPRISE
SEARCH
ARTIFICIAL
INTELLIGENCE (AI)
Fast Track to AI-Powered Findability
Taxonomy
Ontology
Knowledge Graph
Taxonomies, Ontologies, and Knowledge Graphs are your tickets to the fast track!
@EKCONSULTING

19. Laying the Tracks for Search-focused AI
Solutions
Priority Use Cases
Plan for real-world,
priority use cases for
content findability
Knowledge Sources
Design clear
strategies for sourcing
the knowledge that AI
requires from the
organization
Tracking Progress
and Success
Leverage KPIs to track
direct business
impact
Business Case
Build a business case
for KM and AI that is
visible to company
leadership
Semantic Model
Design
Leverage a semantic
layer that includes
comprehensive
taxonomy, metadata,
and ontology models
Proof of Concept
Leverage a semantic
layer that includes
comprehensive
taxonomy, metadata,
and ontology models
Enterprise AI
@EKCONSULTING

20. Product
Vision and
Success
Knowledge
Modeling and
AI
Data and
Infrastructure
Engineering
Information Analyst
Taxonomist
Ontologist
Data Scientist
AI Engineer
ML Engineer
System Admin / IT
Professional
Semantic and Data
Engineer
Product Manager
Business Stakeholder
Subject Matter Expert
Business Analyst
Train Crew and Tools
Tools for Search-based AI Solutions:
● Taxonomy Management System
● Ontology Management System
● Graph Database
● Search Tool
@EKCONSULTING

21. Your Tickets for the Fast Track Train
Agile Approach for Taxonomy & Ontology Designs
Vision &
Planning
Analysis Design Validation
Implementation
& Maintenance
⬢ Alignment across
project sponsors and
key stakeholders
⬢ Business value
definition
⬢ Use case definition
⬢ Persona
development
⬢ Background
information review
⬢ Stakeholder
workshop, focus
groups, and
interviews
⬢ Systems &
Applications Review
⬢ Data inventory
⬢ Core elements
identification
⬢ Inference and
reasoning needs
determination
(ontology)
⬢ Draft model
⬢ Formalize model in
a taxonomy/
ontology tool
⬢ Validation that model
matches use cases and
expert domain
understanding
⬢ Review of model against
best practices
⬢ Data fit check
⬢ Verification that
organizational
information
requirements (cyber
security, PII, etc.) are met
⬢ Installation /
configuration of
model in the
semantic layer
⬢ Documentation
⬢ Governance Plan
⬢ Application of
model
@EKCONSULTING

22. QUESTIONS

23. Contact us
Sara Mae O’Brien-Scott (she/her)
Senior Semantic Engineering Consultant
Enterprise Knowledge (EK)
sobrien-scott@enterprise-knowledge.com
Tatiana Baquero Cakici (she/her)
Senior KM Consultant and Taxonomist
Enterprise Knowledge (EK)
tcakici@enterprise-knowledge.com