1.
KNOWLEDGE GRAPHS AS A PILLAR TO AI
Yanko Ivanov and James Midkiff
May 23, 2018

2.
TABLE OF CONTENTS
ONTOLOGY + GRAPH DATABASE = KNOWLEDGE GRAPH
USE CASE 1: KNOWLEDGE GRAPH AS A RECOMMENDATION ENGINE
USE CASE 2: HIGH PRECISION AUTO-TAGGING

3.
TAXONOMIES, ONTOLOGIES, AND
KNOWLEDGE GRAPHS AS PART OF AI

4.
KNOWLEDGE ORGANIZATION CONTINUUM
FOLKSONOMY
Free-text tags.
CONTROLLED LIST
List of pre-defined terms.
Improves consistency.
THESAURUS
Pre-defined terms & synonyms.
Hierarchical relationships.
Associative (“related to”)
relationships.
Scope notes.
Increased expressiveness.
TAXONOMY
Pre-defined terms & synonyms.
Hierarchical relationships.
Improves consistency.
Allows for parent/child content
relationships.
ONTOLOGY
Scope notes.
Pre-defined classes &
properties.
Expanded relationship types.
Increased expressiveness.
Semantics. Inference.

5.
BUSINESS ONTOLOGY
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.

6.
GRAPH DATABASE
▪ 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”
▪ Consists of triples
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
… … …

7.
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

8.
EXAMPLE USE CASE #1:
PEOPLE AND PRESENTATIONS

9.
PEOPLE AND PRESENTATIONS (P & P)
People Presentations

10.
P & P: ATTRIBUTES AND RELATIONS
People Presentations
Name
Job Title
Employer
[…]
Title
Description
Topics*
Attend(s)
Has Speaker
Spoke At

11.
P & P: EXAMPLE GRAPH
CS203
.
.
Knowledge Graphs
as a Pillar to AI
Attend
James Midkiff
Developer
Yanko Ivanov
Senior KM Consultant
Audience Member
[Role]

12.
P & P: OUR PRESENTATION TOPICS
Knowledge Graphs
Artificial Intelligence
Ontology Design
Taxonomy Design
Machine Learning
Graph Technology
Recommendation Engine
Semantic AI
Audience Member
Has Interest

13.
P & P: SIMILARITY BY INTEREST
How similar are you to the person next to you?
How many interests do you share?
How many unique interests do you both have total?
Jaccard Index "coefficient de communauté" by Paul Jaccard

14.
P & P: PROBABILITY FOR TOPICS
P(A,T) = Probability that Audience Member A has an interest in Topic T
Knowledge Graphs
Artificial
Intelligence
Ontology &
Taxonomy Design
Machine LearningGraph Technology
Recommendation
Engine
Semantic AI

15.
P & P: PROBABILITY FOR PRESENTATIONS
P(A,P) = Probability that Audience Member A would attend Presentation P
CS203
Attend

16.
P & P: RECOMMENDATION ADD-ONS
Weighted Interests
• Scale based on
• Frequency
• Time
User Input and/or
Feedback
• User specifies topics
• User liked or disliked
recommendation
Auto-Tagging
Presentations
• Manual tagging is
inconsistent
• Text extraction
provides context

17.
EXAMPLE USE CASE #2:
AUTO-TAGGING

18.
AUTO-TAGGING
▪ Problem (Context) Specific
Knowledge Graph
▪ Ontology for Content Tagging
▪ Enables Data Analysis on
all Content
Taxonomy Content
Tag

19.
AUTO-TAGGING EXTENSIONS
▪ Enhanced Auto-Tagging
▪ History of Documents
▪ Implicit Auto-Tagging
▪ Associate Taxonomy Terms
▪ Classification
▪ Group Content based on Tags
Taxonomy Content
Tag
Co-occurrence

20.
CONTACT US
WWW.LINKEDIN.COM/IN/YANKOIVANOV
JMIDKIFF@ENTERPRISE-KNOWLEDGE.COMYIVANOV@ENTERPRISE-KNOWLEDGE.COM
WWW.LINKEDIN.COM/IN/JAMESMIDKIFF
Yanko Ivanov James Midkiff
..