Maya Natajaran GraphSummit 2022

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

2. © 2022 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2021
A small sample of Neo4j Knowledge Graph EMEA 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

3. © 2022 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2021
3
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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4
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Organizing
Principle
Relationships

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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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6
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Relationship
s

7. © 2022 Neo4j, Inc. All rights reserved.
7
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Organizin
g
Principle
Relationship
s

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8
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

9. © 2022 Neo4j, Inc. All rights reserved.
9
Driving Intelligence into Data with Knowledge Graphs
Data Graph
Dynamic Context
Knowledge Graph
Deep Dynamic Context
Organizin
g
Principle
Relationship
s

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10
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
TECHNOLOGY
USE
CASES
BUSINESS
USE
CASES
Data Governance
Data Compliance
Risk Management
X-Journey Analytics
Churn Analysis
Fraud
Recommendation
Risk Analysis
Track & Trace
Next Best Action
What-If Analysis
Impact Analysis
X-360
X-Journey
X-Discovery
Recommendation
Personalization
Augmented MDM
Anti-Money Laundering
Identity & Access Mgmt
Network & IT Operations
Root Cause Analysis
Entity Resolution
KG Completion
Predictive Models
Data Catalog
Data Lineage
Data Provenance
Path-Finding
Community Detection
Influencer Identification
Similarity
Graph-Based Search
Link Prediction
Node Classification
Feature Engineering
Data Fabric

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11

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12
Graph Analytics Power 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
What skills are we
most understaffed
in?
How can we find the
best replacement for
an individual who has
left the company?
Can we predict
the next career
move for an
individual?
Fraud Detection

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13
DATA LAKES
13
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
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

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14
Putting Knowledge Graphs to Work in EMEA
EL Corte Inglés
Recommendations
• Challenge: Existing solution could
not offer real-time recommendations
• Solution: Neo4j Knowledge Graph
• 1.5M promotions/day totally
transforming company’s real-time
promotion engine
Zurich Insurance
Fraud Detection
• Challenge: Legacy technology could
not take dependencies into account to
detect and analyze train journeys
• Solution: Neo4j Knowledge Graph
• Identified bottlenecks and heavily
used track sections and best paths
based on distance, cost and train
suitability
Deutsche Bahn
Track & Trace
• Challenge: Needed a quick way to
identify fraud in a flood of automatically
generated alerts.
• Solution: Neo4j Knowledge Graph
• 50,000 hours/ year of investigators’ time
saved

15. © 2022 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
15
Thank You!
Contact us at
sales@neo4j.com

16. © 2022 Neo4j, Inc. All rights reserved.
Neo4j, Inc. All rights reserved 2021
16
Resources
Graph Resources
● Neo4j Knowledge Graph Homepage: neo4j.com/knowledge-graph
● White Paper: From Graphs to Knowledge Graphs: A Short Journey to Unlimited Insights
● Video: Advantages of Graph Technology | https://neo4j.com/videos/why-graph/
● Case Study: UBS | https://neo4j.com/case-studies/ubs-case-study/
● Case Study: Meredith Corporation | https://neo4j.com/users/meredith-corporation/
Neo4j Bookshelf
● Graph Databases For Dummies | https://neo4j.com/graph-databases-for-dummies/
● Graph Data Science For Dummies | https://neo4j.com/graph-data-science-for-dummies/
● O’Reilly Knowledge Graphs: Data in Context for Responsive Businesses |
https://neo4j.com/knowledge-graphs-data-in-context-for-responsive-businesses/