This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.

1. INTRODUCTION TO KNOWLEDGE GRAPHS
A Presentation for Data Summit Connect 2020
June 8, 2020

2. INTRODUCTIONS
SARA NASH
TECHNICAL ANALYST
ENTERPRISE KNOWLEDGE
JOE HILGER
COO AND PRINCIPAL CONSULTANT
ENTERPRISE KNOWLEDGE
@EKCONSULTING

3. AGENDA
@EKCONSULTING
1 2 3
Foundations of
Knowledge
Graphs
Knowledge
Graphs for
Artificial
Intelligence
Use Cases for
Knowledge
Graphs

4. WHAT YOU WILL LEARN TODAY
@EKCONSULTING
How to build a business case for Knowledge Graphs and Enterprise AI
The foundations and technical infrastructure to make Knowledge Graphs a reality
Practical use cases for Knowledge Graphs: Recommendation Engine, Natural Language
Querying, Relationship Discovery, Data Management
Where to begin in Knowledge Graph development – developing an ontology

5. WHAT IS A KNOWLEDGE GRAPH

6. 90% of the data and information we have
today was created just in the past two
years.
Most organizations are built to organize and
manage data and information by type and
department or business function. 80% of
leaders say their systems don’t talk to each
other.
Over 85% of the content and information we
work with is unstructured.
CONFRONTING TODAY’S INFORMATION MANAGEMENT CHALLENGES
90%
AI is set to be the key source of
transformation, disruption, and competitive
advantage in today’s fast changing economy,
contributing to 45% of total economic gains
by 2030.
@EKCONSULTING

7. FOLKSONOMY
Free-text tags.
CONTROLLED LIST
List of pre-defined terms.
Improves consistency.
TAXONOMY
Pre-defined terms &
synonyms.
Hierarchical relationships.
Improves consistency.
Allows for parent/child
content relationships.
Capture related data.
Integration of structured and
unstructured information.
Linked data Store.
Architecture and data
models to enable machine
learning (ML) and other AI
capabilities. Drive efficient
and intelligent data and
information management
solutions.
ONTOLOGY
Predefined classes &
properties.
Expanded relationship types.
Increased expressiveness.
Semantics. Inference.
KNOWLEDGE ORGANIZATION CONTINUUM
@EKCONSULTING
KNOWLEDGE GRAPHS

8. tax·on·o·my (tāk-sōn-mē)
n. pl. tax·on·o·mies
1. The classification of organisms in an
ordered system that indicates natural
relationships.
2. The science, laws, or principles of
classification; systematics.
3. Division into ordered groups or categories:
"Scholars have been laboring to develop a
taxonomy of young killers" (Aric Press).
EK’s Definition of Taxonomy
Controlled vocabularies used to describe or characterize explicit
concepts of information, for purposes of capture, management,
and presentation.
BUSINESS TAXONOMY
@EKCONSULTING

9. A defined data model that describes structured
and unstructured information through:
• entities,
• their properties,
• and the way they relate to one another.
• Ontology is about things, not strings.
• Ontologies model your domain in a machine and
human understandable format.
• Ontologies provide context.
• Effective ontologies require a deep understanding
of the knowledge domain.
BUSINESS ONTOLOGY
@EKCONSULTING

10. § A knowledge graph is a specialized graph or
network of the things we want to describe and
how they are related
§ It is a semantic model since we want to
capture and generate meaning with the model
“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
Google’s knowledge graph is a popular
use case
KNOWLEDGE GRAPH
@EKCONSULTING

11. ONTOLOGY VS. KNOWLEDGE GRAPH
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12. § Consists of triples
§ concept → relationship → concept
§ A linked data store that organizes structured
and unstructured information through:
§ entities,
§ their properties,
§ and relationships.
§ Also known as:
§ Linked Data Store (LD Store)
§ Triple Store
§ “Knowledge Graph”
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
… … …
GRAPH DATABASE
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13. Content & Data
Sources
Subject Predicate Object
Person B isPMOn Project A
Document C isAbout Topic D
Document C isAbout Topic F
Person B IsExpertIn Topic D
Business Ontology
Triple Store/Graph Database
Enterprise Knowledge Graph
Person B
Project A
Document C
Person F
Topic D
Topic E
Business Taxonomy
HOW IT ALL FITS TOGETHER
@EKCONSULTING

14. Resource
Description
Framework
SQL, SAP
Structured
Data Source
Semantic
Enterprise
Search
Analytics
Enterprise
Knowledge Graph
CMS, DMS,
CRM, etc.
Taxonomy &
Auto-tagging
Resource
Description
Framework
Unstructured
Data Source
Question
Answering
SQL, SAP,
Excel
Structured
Data Source
Virtual
Mapping
ENTERPRISE KNOWLEDGE GRAPH
@EKCONSULTING

15. ARTIFICIAL INTELLIGENCE (AI)

16. ARTIFICIAL
INTELLIGENCE (AI)
IN ACTION
AI FOR DATA AND INFORMATION MANAGEMENT
ENTAILS LEVERAGING MACHINE CAPABILITIES TO
DISCOVER AND DELIVER ORGANIZATIONAL
KNOWLEDGE AND INFORMATION IN A WAY THAT
CLOSELY ALIGNS WITH HOW WE LOOK FOR AND
PROCESS INFORMATION.
@EKCONSULTING

17. @EKCONSULTING
DECONSTRUCTING AI: DRIVERS
BUSINESS AGILITY AGING INFRASTRUCTUREDATA DYNAMISM
Volume and dynamism of
organizational data/content
(structured and unstructured)
Growing digitalization, aging
of systems and disparate
sources
User experience, knowledge
loss, bad info/data, data team
efficiency

18. DECONSTRUCTING AI: MACHINE LEARNING
Inferred
Relationships
Automatically discover
implicit facts in your
data
Clustering
Detect fraud, identify
risk factors, categorize
customer behavior
Auto-
Classification
Automatically route
incoming requests
to appropriate
channels
Machine Learning
Image & Shape
Recognition
Digital Asset
Management, product
identification, security,
intelligence
Predictive Analytics
Customer retention, risk modeling,
predictive maintenance
Recommendation
Engine
Discover new content and
information based on
context at the point of need
Natural Language
Processing
Simplify user experience,
bring data closer to
business users
@EKCONSULTING

19. Aggregation, Reasoning, and
Optimization
Graphs allow for aggregation of information from
multiple disparate solutions, which allows
users to find information that exists in multiple
locations, and optimizes data management
and governance.
ENTERPRISE KNOWLEDGE GRAPHS & AI
Understanding Context
Relationships between information give us a
better understanding of how things fit
together, adding knowledge to data.
Structured and Unstructured
Information
Allows for the organization and integration of
structured and unstructured information so that
users can search for data and content at the
same time.
Intuitive Interactions
Graphs store information in the way people
speak and process information, while
simultaneously making it machine readable
and therefore ready for human centered
applications, such as natural language search.
Discover Hidden Facts & Patterns
Inferencing allows for large scale analysis and
identification of related topics and things.
@EKCONSULTING

20. USE CASES FOR
KNOWLEDGE GRAPHS

21. USE CASE #1: RECOMMENDATION ENGINE

22. SLIDE WITH CIRCLE PHOTO
The Business Challenge
A global development bank needed a better
way to disseminate information and
expertise to all of their staff so that they
could complete projects more efficiently,
without rework and knowledge loss.
Their information and expertise were
contained in thousands of unstructured
documents and publications that needed to
be better organized and made accessible.
The Solution
ü EK developed a semantic hub, leveraging a knowledge graph that
collects organizational content, user context, and project activities.
ü The information powered a recommendation engine that suggests
relevant articles and information when an email or a calendar invite is sent
on a given topic or during searches on that topic, which will eventually
power a chatbot as part of a larger AI Strategy.
ü These outputs were then published on the bank’s website to help
improve knowledge retention and to showcase expertise via Google
recognition and search optimization for future reference.
Outcomes
Using knowledge graphs based on this linked data strategy enabled the bank
to connect all of their knowledge assets in a meaningful way to:
§ Increase the relevancy and personalization of search.
§ Enable employees to discover content across unstructured content types,
such as webinars, classes, or other learning materials based on factors
such as location, interest, role, seniority level, etc.
§ Further facilitate connections between people who share similar interests,
expertise, or location.
@EKCONSULTING
USE CASE #1: RECOMMENDATION ENGINE

23. @EKCONSULTING
USE CASE #1: RECOMMENDATION ENGINE
Graph Database

24. Because of a Knowledge Graph…
ü Ability to support future business questions and
needs that are currently unknown
ü Greater flexibility to quickly modify and improve
data flows aligned to business needs
ü Flexibility to add new data sources without
making extensive changes to data architectures
and schemas resulting in rapid iteration and
quick adaptation to changing requirements
ü Architecture allows to quickly iterate and grow
new products and services for its users
@EKCONSULTING
Recommendation Engine

25. USE CASE #2: NATURAL LANGUAGE QUERYING ON
STRUCTURED DATA

26. SLIDE WITH CIRCLE PHOTO
The Business Challenge
One of the largest supply chains needed to
provide its business users a way to obtain quick
answers based on very large and varied data
sets.
The data sets were stored in a large RDBMS data
warehouse with little to no context, making it
difficult to understand its value, which information
to use, and what questions it could answer.
The goal was to bring meaningful information
and facts closer to the business to make
funding and investment decisions.
The Solution
ü By extracting topics, places, people, etc. from a given file, EK developed
an ontology to describe the key types of things business users were
interested in and how they relate to each other. EK mapped the various
data sets to the ontology and leveraged semantic Natural Language
Processing (NLP) capabilities to recognize user intent, link concepts,
and dynamically generate the data queries that provide the response.
Outcomes
In doing so, non-technical users were able to uncover the answers to
critical business questions such as:
§ Which of your products or services are most profitable and perform
better?
§ What investments are successful, and when are they successful?
§ How much of a given product did we deliver in a given timeframe?
§ Who were my most profitable customers last year?
§ How can we align products and services with the right experts,
locations, delivery method, and timing?
@EKCONSULTING
USE CASE #2: NATURAL LANGUAGE QUERYING

27. FVC & LVC
Data Virtual
Graph
Mapping
Graph Search
Knowledge Graph IDE
Configure
Graph
Mapping
Query Graph Data
Connects to
Graph DB
Virtualizes
Relational Data
Data SME
Taxonomy &
Ontology Manager
SPARQL
Knowledge
Graph
Business User
Front End UI
Relational
NoSQL
Metadata
External
Internal
Chatbot
Q&A
Semantic
Enterprise
Search
NLP
@EKCONSULTING
USE CASE #2: NATURAL LANGUAGE QUERYING

28. Because of a Knowledge Graph…
@EKCONSULTING
ü Rapid alignment of data elements with natural
language structure of English questions to
identify user intent
ü Flexible mapping of disparate data source
schemas into a single, unified data model that is
“whiteboardable”- accessible to both technical
and nontechnical users
ü Clear definition of key information entities and
their relationships to each other to unleash the
value of data contexts and meaning
Natural Language Querying on Structured Data

29. USE CASE #3: RELATIONSHIP DISCOVERY THROUGH
UNSTRUCTURED DATA

30. The Business Challenge
A federally funded research and development
center (FFRDC) has an extensive “project
library” where they store technical documents,
certifications, and reports related to various
engineering projects.
These documents often don’t have much
associated metadata and are very difficult
to search. When employees start working on
new projects, it’s hard to tell, from the project
libraries, what was done on previous
projects and who did the work.
@EKCONSULTING
The Solution
ü Using an existing business taxonomy developed by the
FFRDC, EK led the development of an enterprise
knowledge graph, connecting documents to projects, topics,
and individuals through auto-tagging
ü EK also developed a semantic search platform, enabling
document searches based on context.
Outcome
Using the enterprise knowledge graph, the FFRDC could then
use the semantic search application to
§ Browse documents by person, project, and topic
§ Analyze relationships between people and projects directly
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA

31. ▪ Enhanced Auto-Tagging
▪ History of Documents
▪ Implicit Auto-Tagging
▪ Associate Taxonomy Terms
▪ Classification
▪ Group Content based on Tags
Taxonomy Content
Tag
Co-occurrence
@EKCONSULTING
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA

32. v
PROJECTS
PEOPLE
TOPICS
showing 53 results for PROJECT X...
Project X
John Doe (25)
Emily Smith (14)
Robert Jones (5)
Topic A (19)
Topic B (11)
Topic C (3)
Related People
Related Topics
@EKCONSULTING
USE CASE #3: RELATIONSHIP DISCOVERY
THROUGH UNSTRUCTURED DATA

33. Because of a Knowledge Graph…
@EKCONSULTING
ü Ability to support future business questions and
needs that are currently unknown
ü Greater flexibility to quickly modify and improve
data flows aligned to business needs
ü Flexibility to add new data sources without
making extensive changes to data architectures
and schemas
ü Architecture allows to quickly iterate and grow
new products and services for its users
RELATIONSHIP DISCOVERY THROUGH UNSTRUCTURED DATA

34. USE CASE #4: DATA MANAGEMENT

35. The Business Challenge
The data scientists and economists at the
Federal Agency were having trouble
connecting siloed data sources to easily
access, interpret and track all the data and
history in order to provide meaningful context
to the Board.
This Agency needed a solution that
enhanced and modernized their metadata
management practices through improved
access and visibility across their data
resources while maintaining the
appropriate security.
@EKCONSULTING
Solution
ü EK led the development of an advanced, semantic metadata
modeling prototype, leveraging a knowledge graph to provide
key contextual and descriptive information that helped map
relationships across the Agency’s regulatory data sources.
ü EK also developed an intuitive front-end user interface that
enabled end-users and data SMEs to explore and access the data
in the model. The model made it easy to find and connect to the
relevant data the business user needs to view key information at a
glance.
Outcome
Data analysts and researchers can now:
§ Access to the Agency’s data resources in a single tool that makes
data stored in multiple locations available without moving or copying
the data.
§ Spend less time tracking or processing data for non-technical users
who can now directly access and explore the data for decision
making.
USE CASE #4: DATA MANAGEMENT

36. Because of a Knowledge Graph…
@EKCONSULTING
ü Achieve powerful alignment between the application
UI and knowledge graph structure allowing the
graph to define the templates that the UI populated
with key data from the graph
ü Encourage the users to explore the information by
traversing relationships that made navigating the
data easy and intuitive
ü Arrange the information from both unstructured
documentation and structured data sources into a
single, structured format
ü Optimize data quality by allowing the analysis of
network effects, through patterns
DATA MANAGEMENT

37. WE’LL BE ANSWERING QUESTIONS NOW
Q A&
THANKS FOR LISTENING
Q & A SESSION

38. CONTACT US
WWW.LINKEDIN.COM/IN/JOSEPH-HILGER-4767131
SNASH@ENTERPRISE-KNOWLEDGE.COMJHILGER@ENTERPRISE-KNOWLEDGE.COM
WWW.LINKEDIN.COM/IN/SARA-G-NASH
Joseph Hilger Sara Nash
@EKCONSULTING