The document discusses knowledge graphs and their growing use and importance. It notes that two-thirds of Neo4j customers have implemented knowledge graphs and 88% of CXOs believe they will significantly improve business outcomes. It provides examples of how knowledge graphs can drive intelligence from data and be used for applications like recommendations, personalization, and root cause analysis. Finally, it discusses how Neo4j is using knowledge graphs for supply chain management, product tracking, and transportation optimization.

1. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
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2. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
The
Knowledge Graph
Explosion
Dr. Maya Natarajan
Sr. Director, Knowledge Graphs

3. © 2022 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2021
A small sample of
Neo4j Knowledge Graph North America Customers
Two-thirds
of Neo4j customers
have implemented knowledge graphs
88%
CXOs believe knowledge graphs will
significantly improve bottom line
Neo4j Customer Segmentation Analysis, 2020
Pulse Survey, 2020

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Neo4j, Inc. All rights reserved 2021
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A knowledge graph is an
interconnected dataset
enriched with meaning so we
can reason about the
underlying data and use it
confidently for complex
decision-making.

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Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Organizing
Principle
Relationships

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Driving Intelligence into Data with Knowledge Graphs
Data

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Complex Data Comes in Many Shapes & Sizes
A B C D E
A B C D E
One-to-Many
Relationships
Across Many
Entities
Small, Wide Data Complex Data Hierarchical & Recursive Data
Many-to-Many
Relationships
Nested Tree
Structures
Recursion (Self-
Joins)
Deep
Hierarchies
Link Inference
(If C relates to A and A relates to E,
then C must relate to E)
Node Similarity
Hidden Data
Legacy Data Frozen Data
Legacy SQL Systems Data Lake Fact Tables Graph Data Science - Machine Reasoning
A
C
E

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Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Relationships

9. © 2022 Neo4j, Inc. All rights reserved.
9
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Organizing
Principle
Relationships

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What is Semantics?
Entity Resolution
& Analysis
Classification
& Tagging
Artificial
Intelligence
C O M P L E X I T Y
Synonym Rings Taxonomy Thesaurus Ontology
Controlled Vocabularies

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11
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Relationships
Organizing
Principle

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Graph Data Science Powers Neo4j Knowledge Graphs
ACTIONING
DATA ANALYTICS
DATA MANAGEMENT
DECISIONING
Aggregate Validate Govern Explore Deduce Infer Forecast Predict Prescribe
DATA ASSURANCE DATA INSIGHT
GRAPH-BASED
ANALYTICS
GRAPH-BASED
MACHINE LEARNING
Risk Management
Recommendation
Personalization
Network and IT Ops
Root Cause Analysis
Impact Analysis
Skills Discovery
How can we staff our
projects with
individuals with the
most relevant skills?
How can we find the
best replacement for
an individual who has
left the company?
How can we
support our
employees’ career
progression?
Fraud Detection

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DATA LAKES
14
USE CASES
INGEST
Knowledge Graphs Connect the Ecosystem
Apache
Spark
Apache
Hop
Structured
Unstructured
DATA
ANALYTICS
DATA
MANAGEMENT
Route Optimization
Patient Journey
Churn Analysis
What-if Analysis
Feature
Engineering
Recommendations
X- 360
Root Cause
Analysis
Data Compliance
Data Governance
Data Provenance
Data Lineage
Fraud Detection
Ontologies
Neo4j Bloom
Visualization
Neo4j Graph
Data Science
KNOWLEDGE GRAPH
PRODUCT COMPONENTS
Cypher/Knowledge Graph Workbench
APOC
AUTO ML
DRIVERS & APIs
DATA ORCHESTRATION DATA CATALOG DATA CONSUMPTION
VISUALIZE
DWH
Connector

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Putting Knowledge Graphs to Work in Supply Chain
• Challenge: Slow, inflexible &
expensive system for buying and
managing of army equipment
• Solution: Neo4j Knowledge Graph
• Quicker, cheaper & more accurate
ordering of parts and 8X faster
complex ”what-if” analyses
JB Hunt
Track & Trace
• Challenge: Difficulty finding at-fault
components due to a decentralized ad-
hoc process
• Solution: Neo4j Knowledge Graph
• 25% faster identification & elimination
of at-fault components.
Boston Scientific
Root Cause Analysis
• Challenge: Driver shortage due to long
detention times and exacerbated by Covid
• Solution: Neo4j Knowledge Graph
• 6M empty miles saved for drivers
60% reduction in carbon footprint
US Army
Bill of Materials

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Neo4j, Inc. All rights reserved 2021
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Thank you!
Contact us at
sales@neo4j.com

17. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
17
Thank you!
Contact us at
sales@neo4j.com