This presentation from Joe Hilger, Founder and COO of Enterprise Knowledge was presented at the KM Showcase 2020 in Arlington, VA on March 5th. The presentation addresses why knowledge management is the foundation for successful artificial intelligence. Hilger provides reasoning and examples for why taxonomy, content strategy, governance, and KM leadership are foundational requirements for organization's pursuing recommender systems, chat bots, and much more. Lastly, he defines Knowledge Artificial Intelligence and provides a brief overview of knowledge graphs.

1. LEVERAGING KM AS THE FOUNDATION FOR AI
2020 KM Symposium
March 5, 2020
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2. KNOWLEDGE TO AI

3. Highly internalized,
gained through
professional,
educational, and
personal experience
that has not yet
been recorded or
captured.
Knowledge that has been
made visible by capturing,
recording, or embedding it in
databases, documents, and
processes.
Structured
Easy for systems and
machines to read and process.
More difficult for human users
to understand without
underlying context.
Unstructured
Generally easy for human
users to read and understand,
but more difficult for machines
to use and process.
Tacit Explicit
FORMS OF KNOWLEDGE

4. FIND
CAPTURE
ACT
KM LIFECYCLE
Knowledge and Information
Management (KM) efforts exist on
the same spectrum, with
knowledge moving from tacit to
explicit, and then content moving
from unstructured to structured.
The most effective KM efforts are
those that are driven by business
value and end user needs.
KM can offer immediate value while
laying the foundation for advanced
search, customized knowledge
experience, and artificial intelligence.
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5. BUSINESS OUTCOMESKM OUTCOMES
▪ Improved findability and discoverability of
content.
▪ Less time waiting, searching, and recreating
knowledge.
▪ Increased use and reuse of information.
▪ Decreased knowledge loss.
▪ Improved organizational awareness and
alignment.
▪ Culture of knowledge sharing.
▪ Enhanced quality, availability, and speed of
learning.
▪ Increased awareness of, and connection to,
experts.
▪ Improved productivity.
▪ Decreased cost (and cost avoidance).
▪ Increased employee satisfaction and
retention.
▪ Faster and better up-scaling of
employees.
▪ Improved customer satisfaction and
retention.
▪ Improved execution.
▪ Improved sales.
▪ Increased collaboration and innovation.
▪ Future readiness.
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6. KM FOUNDATIONS
FOR AI
WHY KM MATTERS
CONTENT CLEANUP
CONTENT GOVERNANCE
TAXONOMY DESIGN
CONTENT TYPES
KNOWLEDGE SHARING
CULTURE
KM LEADERSHIP
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7. TAXONOMY DESIGN
WHAT IS TAXONOMY DESIGN? WHAT IS THE VALUE TO AI?
Classification of an
organization's knowledge
and information for the
purposes of findability and
discoverability.
An effective business
taxonomy will span an
enterprise’s information,
users, and potential needs
as it is usable, intuitive, and
natural.
Ensure faceting works and
different types of content from
different sources can be
seamlessly integrated.
Provide a way for the
machine to understand an
organization’s vocabulary.
Taxonomies are the building
blocks to Ontologies, which
are foundational for building
relationships and context.
Ensures an organization
can effectively capture,
manage, and present their
growing store of
information.
Provides structure to
unstructured information.
Informs metadata and
content tagging efforts.
WHAT IS THE
INDEPENDENT VALUE?
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8. CONTENT CLEANUP
WHAT IS CONTENT CLEANUP? WHAT IS THE
INDEPENDENT VALUE?
WHAT IS THE VALUE TO AI?
The review and cleaning of
outdated, obsolete,
incorrect, and duplicative
content.
Determine if content should
be maintained as-is,
updated, archived, or
removed.
Cleaned and enhanced
content, with tags, ensures
the right content surfaces for
the end user and is weighted
appropriately.
AI requires training
material. An organization
needs to provide quality
training material so that it
can get to a high quality
machine.
Improved KM is worthless
without the right information.
Increases productivity as
employees spend less time
shifting through inaccurate
and/or not actionable
content.
Decreases risk as
employees are presented
with accurate content.“Only 20-30% of all content stores should
be maintained during a content migration.”
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9. CONTENT TYPES
WHAT IS A CONTENT TYPE? WHAT IS THE
INDEPENDENT VALUE?
WHAT IS THE VALUE TO AI?
The process of defining
common or standard
templates for the types of
content users interact with
within a specific system.
Includes the definition of
text fields and description
of what information should
be found in each field as
well as the definition of
which metadata fields apply
to a specific template.
Allows AI to surface
appropriate elements of
content in the most flexible
manner possible.
Enables different types of
content to be mapped
intelligently.
Enables granular tagging
for greater relationship
identification.
Standardizes and optimizes
the way content is captured
and presented, ensuring it
remains “fresh” and
accurate to best serve the
needs of the organization.
Decreases the amount of
time required to create new
content.
Increases the readability
and digestibility of content.
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10. CONTENT GOVERNANCE
WHAT IS
CONTENT GOVERNANCE?
WHAT IS THE
INDEPENDENT VALUE?
WHAT IS THE VALUE TO AI?
A common set of standards
and processes that are
designed to maintain and
consistently improve the
content over time.
An effective governance
plan includes:
- Business Case
- Roles and Responsibilities
- Policies and Procedures
- Communication, Education,
and Marketing
Ensures accuracy of the
content.
Ensures the accuracy of
the ontology model.
Enables the appropriate
controls and flexibility to
serve evolving needs, user
behaviors, and clearer user
understanding.
Enables ongoing content
management and
continuous improvement of
content over time.
Increases the effectiveness
of the organization’s
content strategy efforts in
order to meet its business
objectives.
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11. KM LEADERSHIP
WHAT IS KM LEADERSHIP? WHAT IS THE
INDEPENDENT VALUE?
WHAT IS THE VALUE TO AI?
Enables and promotes
knowledge processes such
as knowledge creation,
transfer, and dissemination,
and creates
environment/culture/
atmosphere which
stimulates and facilitates
active engagement in these
processes.
With KM Leadership, an
organization can better
strategize, budget, and
champion for the
implementation of the KM
elements foundational for
actionable AI.
Promotes the acceptance
of a new culture, drives
adoption, and integrated
change.
Promotes the value of
sound and systematic
knowledge sharing by
modeling best-practices,
signaling the strategic
importance of the
investment.
Encourages, recognizes,
and rewards knowledge
sharing in support of the
sustainment of KM efforts.
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12. KNOWLEDGE SHARING CULTURE
WHAT IS A KNOWLEDGE
SHARING CULTURE?
WHAT IS THE
INDEPENDENT VALUE?
WHAT IS THE VALUE TO AI?
An organization that builds
knowledge sharing into the
day-to-day behaviors and
actions of its workforce.
Includes user-centric
processes and concepts,
such as Gamification, to
ignite collaboration and
learning.
Surfaces the newly shared
knowledge in a form that is
easy to find and discover.
All of the right knowledge is
seeded in the right place to
add the value.
Knowledge must be captured
to be related and surfaced.
Faster knowledge
dissemination.
Keeps knowledge workers
engaged, increases
productivity and innovation,
and fosters continuous
learning and growth.
Rewards good behavior.
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13. FIVE LEVELS OF KAI
KNOWLEDGE ARTIFICIAL INTELLIGENCE (KAI)
IS THE APPLICATION OF ARTIFICIAL INTELLIGENCE CONCEPTS AND
THEORIES TO ENSURE THE APPROPRIATE CAPTURE, MANAGEMENT,
AND PRESENTATION OF THE FULL SPECTRUM OF KNOWLEDGE AND
INFORMATION.
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14. FIVE LEVELS OF KAI
ANSWER
▪ Basic query/response capabilities.
▪ Action-oriented search, chatbots, and voice assistants.
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15. FIVE LEVELS OF KAI
RECOMMEND
▪ Recommendations based on user behavior, tags,
analytics, and a combination thereof.
▪ “Push” capabilities to go beyond finding and enabling
discovery.
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16. FIVE LEVELS OF KAI
▪ Combination of different types, formats, and sources of
content into a contextualized whole.
▪ Integrates parts and wholes of individual content types in
order to provide a cohesive answer.
COMBINE
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17. FIVE LEVELS OF KAI
INFER
▪ Goes a step beyond Combine and introduces
programmed decision-making logic into the mix.
▪ Triggered based on intelligence or prompts in order to
build new content that doesn’t just answer a query, but
answers a need/want.
▪ Results in the creation of new content, highly customized
for a specific user or case.
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18. FIVE LEVELS OF KAI CONT.
▪ Incorporates predictive capabilities to spot trends, identify
potential risks and opportunities, and provide actionable
guidance leveraging a complete view and understanding
of the knowledge and information that exists.
▪ Generates and surfaces the right knowledge before you
even know you need it.
▪ Spots demographic trends to fill gaps and needs before
they exist.
ADVISE
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19. THE FUTURE SOLUTION
+
ONTOLOGY
KNOWLEDGE GRAPHS
= ARTIFICIAL
INTELLIGENCE
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20. KNOWLEDGE GRAPHS
Another word for graphs are “networks”
Nodes
Edges
• A knowledge graph: a network of the
things we want to describe and how they
are related
• We construct a semantic model since
we want to capture and generate
meaning with the model
Google’s knowledge graph is a
popular use case
“The application of graph processing and graph DBMSs will
grow at 100 percent annually through 2022 to continuously
accelerate data preparation and enable more complex and
adaptive data science.”
– Gartner’s Top 10 Data and Analytics
Technology Trends for 2019
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21. WHAT IS A KNOWLEDGE GRAPH
Content Sources
Subject Predicate Object
Project A hasTitle Title A
Person B isPMOn Project A
Document C isAbout Topic D
Document C isAbout Topic F
Person B IsExpertIn Topic D
… … …
Business Ontology
Graph Database
Enterprise
Knowledge Graph
Business Taxonomy
Person B
Project A
Document C
Person F
Topic D
Topic E
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22. BENEFITS OF A KNOWLEDGE GRAPH
Understanding Context
Relationships between
information allows gives us a
better understanding of how
things fit together so that search
can be more precise.
Structured and
Unstructured Information
Graphs allow for the integration of
structured and unstructured
information so that users can
search for data and content at the
same time.
Natural Language Search
Graphs store information the way
people speak. Integrating a
graph into your search makes
natural language search easier to
implement.
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.
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23. HOW DO I GET STARTED
Educate the organization on how AI can and should work.
Investigate the products and tools that you have access to that
can automate information management.
Inventory your content to understand what you have and how to
organize it.
Start small and then grow.
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24. WE’LL BE ANSWERING QUESTIONS NOW
Q A&
THANKS FOR LISTENING
Q & A SESSION
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25. CONTACT US
JOE HILGER
JHILGER@ ENTERPRISE-KNOWLEDGE.COM
571-436-0271
ZACH WAHL
ZWAHL@ ENTERPRISE-KNOWLEDGE.COM
571-800-9803