1.
Digital Transformation with Graphs,
Ontology, and ML Ops at Northrop
Grumman
7 December 2022
Data Scientist
Zachary Tretter
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Steven Scott
Cognitive Software Engineer
Approved for Public Release: NG22-2209 © 2022, Northrop Grumman

2.
• Data Challenges for Digital Transformation
• Methodology – Ontology Driven Design
• Applications of Ontology Driven Design
Outline
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3.
About Northrop Grumman
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4.
Data Problems
Siloed
Dirty
Incomplete
Rigid
Obscure
Difficult to Understand
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7.
Drowning in data
but starving for knowledge
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8.
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Disparate Data + SME Knowledge Model = Context-Rich Knowledge Graph
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9.
Ontology-Driven Design
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10.
“Software should be governed by
knowledge, not by code”
- The ODD Design Philosophy
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11.
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12.
• Identify what things exist
• Identify relationships between
those things
• Disambiguate between
conflated terms
• Map Data Sources to Graph
Structures to Questions
How to Build an Ontology
purchases
actor actor
relationship
axiom
User shoes
User purchases shoes
“Our users generally
purchase shoes from us
every 11 months”
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13.
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Thou Shalt
A…
Thou Shalt
B…
Thou Shalt
C…
Thou Shalt
D…
Thou Shalt
E…
IMPERATIVE
DECLARATIVE
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14.
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Thou Shalt
A…
Thou Shalt
B…
Thou Shalt
C…
Thou Shalt
D…
Thou Shalt
E…
IMPERATIVE
DECLARATIVE
ASYMMETRIC
MAPPING
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15.
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CODE
F
C
B
E
D
A
ONTOLOGY
DECLARATIVE IMPERATIVE
SYMMETRICAL
MAPPING!
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16.
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F
C
B
E
D
A
IMPERATIVE
DECLARATIVE
SYMMETRICAL
MAPPING!
Thou Shalt
A…
Thou Shalt
B…
Thou Shalt
C…
Thou Shalt
D…
Thou Shalt
E…
Requirements Ontology
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17.
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18.
ure dolor in reprehenderit
101010010101010
y”},{obj:”property”},{obj:”property”},{obj:”property”},{obj:”property”}, {obj:”property”},{obj:”property”},{obj:”property”},{obj:”property”},{obj:”property”},{obj:
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20.
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27.
Build Knowledge Models
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28.
Build Knowledge Models
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29.
Derive Mapping Pipelines
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30.
Derive Mapping Pipelines
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31.
Analyze Smart Data
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32.
Our Graph and
Ontology-Driven Design
Projects
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33.
Digital Twins
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Compilation of Sensor
Driven observations
informing current state
The ideal design created
by engineers and
planners prior to
realization
Ideal State
Twins
Actual State
Twins
Projected future state
based on analytics
Potential State
Twins
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34.
Digital Twinning
• Buildings, rooms,
equipment,
employees
• View seating,
conference room use
• Planning changes
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35.
Fleet Management
Maintenance
Data
Sensor
Data
System
Understanding
Reliability
Sustainability
Supply Chain
Supportability
Optimized Solution
Budget
Desired
Availability
Manpower
Constraints
Customer Driven
Cascading Predictive Analysis
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36.
Master Data Management
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37.
Master Data Management
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38.
Discourse Graphs
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39.
Questions?
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